{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forest Cover Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, I will make use of the Forest Cover dataset to try to predict the different Cover Types given the previded features. I will successivelly try differerent feature elimination techniques to see how this can affect training times and overall model accuracy. <br>\n",
    "\n",
    "Reducing the number of features in a dataset, can lead to:\n",
    "\n",
    "- Accuracy improvements\n",
    "- Overfitting risk reduction\n",
    "- Speed up in training\n",
    "- Improved Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/forest-cover-type-dataset/covtype.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.pyplot import figure\n",
    "from sklearn import preprocessing\n",
    "import time\n",
    "import os\n",
    "\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
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      "text/plain": [
       "   Elevation  Aspect  Slope  Horizontal_Distance_To_Hydrology  \\\n",
       "0       2596      51      3                               258   \n",
       "1       2590      56      2                               212   \n",
       "2       2804     139      9                               268   \n",
       "3       2785     155     18                               242   \n",
       "4       2595      45      2                               153   \n",
       "\n",
       "   Vertical_Distance_To_Hydrology  Horizontal_Distance_To_Roadways  \\\n",
       "0                               0                              510   \n",
       "1                              -6                              390   \n",
       "2                              65                             3180   \n",
       "3                             118                             3090   \n",
       "4                              -1                              391   \n",
       "\n",
       "   Hillshade_9am  Hillshade_Noon  Hillshade_3pm  \\\n",
       "0            221             232            148   \n",
       "1            220             235            151   \n",
       "2            234             238            135   \n",
       "3            238             238            122   \n",
       "4            220             234            150   \n",
       "\n",
       "   Horizontal_Distance_To_Fire_Points  Wilderness_Area1  Wilderness_Area2  \\\n",
       "0                                6279                 1                 0   \n",
       "1                                6225                 1                 0   \n",
       "2                                6121                 1                 0   \n",
       "3                                6211                 1                 0   \n",
       "4                                6172                 1                 0   \n",
       "\n",
       "   Wilderness_Area3  Wilderness_Area4  Soil_Type1  Soil_Type2  Soil_Type3  \\\n",
       "0                 0                 0           0           0           0   \n",
       "1                 0                 0           0           0           0   \n",
       "2                 0                 0           0           0           0   \n",
       "3                 0                 0           0           0           0   \n",
       "4                 0                 0           0           0           0   \n",
       "\n",
       "   Soil_Type4  Soil_Type5  Soil_Type6  Soil_Type7  Soil_Type8  Soil_Type9  \\\n",
       "0           0           0           0           0           0           0   \n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "3           0           0           0           0           0           0   \n",
       "4           0           0           0           0           0           0   \n",
       "\n",
       "   Soil_Type10  Soil_Type11  Soil_Type12  Soil_Type13  Soil_Type14  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            1            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   Soil_Type15  Soil_Type16  Soil_Type17  Soil_Type18  Soil_Type19  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   Soil_Type20  Soil_Type21  Soil_Type22  Soil_Type23  Soil_Type24  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   Soil_Type25  Soil_Type26  Soil_Type27  Soil_Type28  Soil_Type29  \\\n",
       "0            0            0            0            0            1   \n",
       "1            0            0            0            0            1   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            1   \n",
       "\n",
       "   Soil_Type30  Soil_Type31  Soil_Type32  Soil_Type33  Soil_Type34  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            1            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   Soil_Type35  Soil_Type36  Soil_Type37  Soil_Type38  Soil_Type39  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   Soil_Type40  Cover_Type  \n",
       "0            0           5  \n",
       "1            0           5  \n",
       "2            0           2  \n",
       "3            0           2  \n",
       "4            0           5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/kaggle/input/forest-cover-type-dataset/covtype.csv')\n",
    "pd.options.display.max_columns = None\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percent_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Elevation</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type28</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type17</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type18</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type19</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type20</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type21</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type22</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type23</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type24</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type26</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type27</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type29</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type30</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type31</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type32</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type33</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type34</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type35</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type36</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type37</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type38</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type39</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Aspect</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wilderness_Area4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slope</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Horizontal_Distance_To_Hydrology</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vertical_Distance_To_Hydrology</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Horizontal_Distance_To_Roadways</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hillshade_9am</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hillshade_Noon</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hillshade_3pm</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Horizontal_Distance_To_Fire_Points</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wilderness_Area1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wilderness_Area2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wilderness_Area3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type13</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type11</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Soil_Type12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cover_Type</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    percent_missing\n",
       "Elevation                                       0.0\n",
       "Soil_Type28                                     0.0\n",
       "Soil_Type17                                     0.0\n",
       "Soil_Type18                                     0.0\n",
       "Soil_Type19                                     0.0\n",
       "Soil_Type20                                     0.0\n",
       "Soil_Type21                                     0.0\n",
       "Soil_Type22                                     0.0\n",
       "Soil_Type23                                     0.0\n",
       "Soil_Type24                                     0.0\n",
       "Soil_Type25                                     0.0\n",
       "Soil_Type26                                     0.0\n",
       "Soil_Type27                                     0.0\n",
       "Soil_Type29                                     0.0\n",
       "Soil_Type15                                     0.0\n",
       "Soil_Type30                                     0.0\n",
       "Soil_Type31                                     0.0\n",
       "Soil_Type32                                     0.0\n",
       "Soil_Type33                                     0.0\n",
       "Soil_Type34                                     0.0\n",
       "Soil_Type35                                     0.0\n",
       "Soil_Type36                                     0.0\n",
       "Soil_Type37                                     0.0\n",
       "Soil_Type38                                     0.0\n",
       "Soil_Type39                                     0.0\n",
       "Soil_Type40                                     0.0\n",
       "Soil_Type16                                     0.0\n",
       "Soil_Type14                                     0.0\n",
       "Aspect                                          0.0\n",
       "Wilderness_Area4                                0.0\n",
       "Slope                                           0.0\n",
       "Horizontal_Distance_To_Hydrology                0.0\n",
       "Vertical_Distance_To_Hydrology                  0.0\n",
       "Horizontal_Distance_To_Roadways                 0.0\n",
       "Hillshade_9am                                   0.0\n",
       "Hillshade_Noon                                  0.0\n",
       "Hillshade_3pm                                   0.0\n",
       "Horizontal_Distance_To_Fire_Points              0.0\n",
       "Wilderness_Area1                                0.0\n",
       "Wilderness_Area2                                0.0\n",
       "Wilderness_Area3                                0.0\n",
       "Soil_Type1                                      0.0\n",
       "Soil_Type13                                     0.0\n",
       "Soil_Type2                                      0.0\n",
       "Soil_Type3                                      0.0\n",
       "Soil_Type4                                      0.0\n",
       "Soil_Type5                                      0.0\n",
       "Soil_Type6                                      0.0\n",
       "Soil_Type7                                      0.0\n",
       "Soil_Type8                                      0.0\n",
       "Soil_Type9                                      0.0\n",
       "Soil_Type10                                     0.0\n",
       "Soil_Type11                                     0.0\n",
       "Soil_Type12                                     0.0\n",
       "Cover_Type                                      0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_missing = df.isnull().sum() * 100 / len(df)\n",
    "missing_values = pd.DataFrame({'percent_missing': percent_missing})\n",
    "missing_values.sort_values(by ='percent_missing' , ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\")\n",
    "f = sns.countplot(x=\"Cover_Type\", data=df, palette=\"bwr\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    283301\n",
       "1    211840\n",
       "3     35754\n",
       "7     20510\n",
       "6     17367\n",
       "5      9493\n",
       "4      2747\n",
       "Name: Cover_Type, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cover_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Cover_Type'], axis = 1)\n",
    "Y = df['Cover_Type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(581012, 54)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f150177ba58>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(15, 20), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "corr= X.corr()\n",
    "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X2 = StandardScaler().fit_transform(X)\n",
    "\n",
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X2, Y, test_size = 0.30, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1468.2401068580002\n",
      "[[43670 18399    47     0     1     0  1282]\n",
      " [15429 67364  1940     3     2   145   110]\n",
      " [    1  1118  9411    68     0   224     0]\n",
      " [    0     0   580   166     0    83     0]\n",
      " [  122  2470   268     0    10     0     0]\n",
      " [    0  1898  3014    23     0   314     0]\n",
      " [ 2877    33    27     0     0     0  3205]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.70      0.69      0.70     63399\n",
      "           2       0.74      0.79      0.76     84993\n",
      "           3       0.62      0.87      0.72     10822\n",
      "           4       0.64      0.20      0.30       829\n",
      "           5       0.77      0.00      0.01      2870\n",
      "           6       0.41      0.06      0.10      5249\n",
      "           7       0.70      0.52      0.60      6142\n",
      "\n",
      "    accuracy                           0.71    174304\n",
      "   macro avg       0.65      0.45      0.46    174304\n",
      "weighted avg       0.71      0.71      0.70    174304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedsvm = svm.LinearSVC().fit(X_Train, Y_Train)\n",
    "print(time.process_time() - start)\n",
    "predictionsvm = trainedsvm.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionsvm))\n",
    "print(classification_report(Y_Test,predictionsvm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.891806234999876\n",
      "[[59198  3821     6     0    49     7   318]\n",
      " [ 3683 80450   243     2   395   175    45]\n",
      " [    5   211 10052   102    27   425     0]\n",
      " [    0     2    95   683     0    49     0]\n",
      " [   64   442    29     0  2321    12     2]\n",
      " [    6   192   487    39    13  4512     0]\n",
      " [  321    50     0     0     0     0  5771]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.94      0.93      0.93     63399\n",
      "           2       0.94      0.95      0.95     84993\n",
      "           3       0.92      0.93      0.93     10822\n",
      "           4       0.83      0.82      0.83       829\n",
      "           5       0.83      0.81      0.82      2870\n",
      "           6       0.87      0.86      0.87      5249\n",
      "           7       0.94      0.94      0.94      6142\n",
      "\n",
      "    accuracy                           0.94    174304\n",
      "   macro avg       0.90      0.89      0.89    174304\n",
      "weighted avg       0.94      0.94      0.94    174304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedtree = tree.DecisionTreeClassifier().fit(X_Train, Y_Train)\n",
    "print(time.process_time() - start)\n",
    "predictionstree = trainedtree.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionstree))\n",
    "print(classification_report(Y_Test,predictionstree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"1168pt\" height=\"401pt\"\n",
       " viewBox=\"0.00 0.00 1168.00 401.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 397)\">\n",
       "<title>Tree</title>\n",
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       "<text text-anchor=\"start\" x=\"560.5\" y=\"-377.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Elevation ≤ 0.304</text>\n",
       "<text text-anchor=\"start\" x=\"578\" y=\"-362.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.623</text>\n",
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       "<text text-anchor=\"start\" x=\"431.5\" y=\"-332.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [148441, 198308, 24932, 1918, 6623, 12118, 14368]</text>\n",
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       "<text text-anchor=\"start\" x=\"395.5\" y=\"-258.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Elevation ≤ &#45;1.603</text>\n",
       "<text text-anchor=\"start\" x=\"415\" y=\"-243.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.552</text>\n",
       "<text text-anchor=\"start\" x=\"394.5\" y=\"-228.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 235814</text>\n",
       "<text text-anchor=\"start\" x=\"283.5\" y=\"-213.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [40268, 149900, 24932, 1918, 6623, 12118, 55]</text>\n",
       "<text text-anchor=\"start\" x=\"423\" y=\"-198.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 2</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\"><title>0&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M556.948,-309.907C543.546,-300.288 529.149,-289.953 515.408,-280.09\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"517.117,-277.009 506.952,-274.021 513.035,-282.695 517.117,-277.009\"/>\n",
       "<text text-anchor=\"middle\" x=\"511.005\" y=\"-294.99\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n",
       "</g>\n",
       "<!-- 28560 -->\n",
       "<g id=\"node9\" class=\"node\"><title>28560</title>\n",
       "<path fill=\"#f2c29e\" stroke=\"black\" d=\"M899,-274C899,-274 656,-274 656,-274 650,-274 644,-268 644,-262 644,-262 644,-203 644,-203 644,-197 650,-191 656,-191 656,-191 899,-191 899,-191 905,-191 911,-197 911,-203 911,-203 911,-262 911,-262 911,-268 905,-274 899,-274\"/>\n",
       "<text text-anchor=\"start\" x=\"724.5\" y=\"-258.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Elevation ≤ 1.283</text>\n",
       "<text text-anchor=\"start\" x=\"742\" y=\"-243.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.512</text>\n",
       "<text text-anchor=\"start\" x=\"721.5\" y=\"-228.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 170894</text>\n",
       "<text text-anchor=\"start\" x=\"652\" y=\"-213.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [108173, 48408, 0, 0, 0, 0, 14313]</text>\n",
       "<text text-anchor=\"start\" x=\"750\" y=\"-198.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;28560 -->\n",
       "<g id=\"edge8\" class=\"edge\"><title>0&#45;&gt;28560</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M670.399,-309.907C683.883,-300.288 698.369,-289.953 712.194,-280.09\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"714.594,-282.678 720.702,-274.021 710.528,-276.979 714.594,-282.678\"/>\n",
       "<text text-anchor=\"middle\" x=\"716.577\" y=\"-294.978\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g id=\"node3\" class=\"node\"><title>2</title>\n",
       "<path fill=\"#aef4b6\" stroke=\"black\" d=\"M271,-155C271,-155 12,-155 12,-155 6,-155 0,-149 0,-143 0,-143 0,-84 0,-84 0,-78 6,-72 12,-72 12,-72 271,-72 271,-72 277,-72 283,-78 283,-84 283,-84 283,-143 283,-143 283,-149 277,-155 271,-155\"/>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-139.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Horizontal_Distance_To_Hydrology ≤ &#45;1.197</text>\n",
       "<text text-anchor=\"start\" x=\"106\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.581</text>\n",
       "<text text-anchor=\"start\" x=\"89\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 29783</text>\n",
       "<text text-anchor=\"start\" x=\"12\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [6, 2301, 17100, 1915, 92, 8369, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"114\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 3</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;2 -->\n",
       "<g id=\"edge2\" class=\"edge\"><title>1&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M343.294,-190.907C315.731,-180.471 285.946,-169.193 257.94,-158.589\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"259.108,-155.289 248.517,-155.021 256.629,-161.835 259.108,-155.289\"/>\n",
       "</g>\n",
       "<!-- 4555 -->\n",
       "<g id=\"node6\" class=\"node\"><title>4555</title>\n",
       "<path fill=\"#d0ee7f\" stroke=\"black\" d=\"M601.5,-155C601.5,-155 313.5,-155 313.5,-155 307.5,-155 301.5,-149 301.5,-143 301.5,-143 301.5,-84 301.5,-84 301.5,-78 307.5,-72 313.5,-72 313.5,-72 601.5,-72 601.5,-72 607.5,-72 613.5,-78 613.5,-84 613.5,-84 613.5,-143 613.5,-143 613.5,-149 607.5,-155 601.5,-155\"/>\n",
       "<text text-anchor=\"start\" x=\"398.5\" y=\"-139.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Soil_Type4 ≤ 3.313</text>\n",
       "<text text-anchor=\"start\" x=\"422\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.446</text>\n",
       "<text text-anchor=\"start\" x=\"401.5\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 206031</text>\n",
       "<text text-anchor=\"start\" x=\"309.5\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [40262, 147599, 7832, 3, 6531, 3749, 55]</text>\n",
       "<text text-anchor=\"start\" x=\"430\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 2</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;4555 -->\n",
       "<g id=\"edge5\" class=\"edge\"><title>1&#45;&gt;4555</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M452.929,-190.907C453.428,-182.558 453.96,-173.671 454.477,-165.02\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"457.972,-165.212 455.076,-155.021 450.985,-164.794 457.972,-165.212\"/>\n",
       "</g>\n",
       "<!-- 3 -->\n",
       "<g id=\"node4\" class=\"node\"><title>3</title>\n",
       "<path fill=\"#c0c0c0\" stroke=\"black\" d=\"M120.5,-36C120.5,-36 90.5,-36 90.5,-36 84.5,-36 78.5,-30 78.5,-24 78.5,-24 78.5,-12 78.5,-12 78.5,-6 84.5,-0 90.5,-0 90.5,-0 120.5,-0 120.5,-0 126.5,-0 132.5,-6 132.5,-12 132.5,-12 132.5,-24 132.5,-24 132.5,-30 126.5,-36 120.5,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"105.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">(...)</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;3 -->\n",
       "<g id=\"edge3\" class=\"edge\"><title>2&#45;&gt;3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M125.815,-71.7615C122.457,-63.0419 118.991,-54.0385 115.893,-45.9921\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"119.061,-44.4797 112.201,-36.4051 112.528,-46.9949 119.061,-44.4797\"/>\n",
       "</g>\n",
       "<!-- 704 -->\n",
       "<g id=\"node5\" class=\"node\"><title>704</title>\n",
       "<path fill=\"#c0c0c0\" stroke=\"black\" d=\"M192.5,-36C192.5,-36 162.5,-36 162.5,-36 156.5,-36 150.5,-30 150.5,-24 150.5,-24 150.5,-12 150.5,-12 150.5,-6 156.5,-0 162.5,-0 162.5,-0 192.5,-0 192.5,-0 198.5,-0 204.5,-6 204.5,-12 204.5,-12 204.5,-24 204.5,-24 204.5,-30 198.5,-36 192.5,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"177.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">(...)</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;704 -->\n",
       "<g id=\"edge4\" class=\"edge\"><title>2&#45;&gt;704</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M157.185,-71.7615C160.543,-63.0419 164.009,-54.0385 167.107,-45.9921\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"170.472,-46.9949 170.799,-36.4051 163.939,-44.4797 170.472,-46.9949\"/>\n",
       "</g>\n",
       "<!-- 4556 -->\n",
       "<g id=\"node7\" class=\"node\"><title>4556</title>\n",
       "<path fill=\"#c0c0c0\" stroke=\"black\" d=\"M436.5,-36C436.5,-36 406.5,-36 406.5,-36 400.5,-36 394.5,-30 394.5,-24 394.5,-24 394.5,-12 394.5,-12 394.5,-6 400.5,-0 406.5,-0 406.5,-0 436.5,-0 436.5,-0 442.5,-0 448.5,-6 448.5,-12 448.5,-12 448.5,-24 448.5,-24 448.5,-30 442.5,-36 436.5,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"421.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">(...)</text>\n",
       "</g>\n",
       "<!-- 4555&#45;&gt;4556 -->\n",
       "<g id=\"edge6\" class=\"edge\"><title>4555&#45;&gt;4556</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M441.815,-71.7615C438.457,-63.0419 434.991,-54.0385 431.893,-45.9921\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"435.061,-44.4797 428.201,-36.4051 428.528,-46.9949 435.061,-44.4797\"/>\n",
       "</g>\n",
       "<!-- 27823 -->\n",
       "<g id=\"node8\" class=\"node\"><title>27823</title>\n",
       "<path fill=\"#c0c0c0\" stroke=\"black\" d=\"M508.5,-36C508.5,-36 478.5,-36 478.5,-36 472.5,-36 466.5,-30 466.5,-24 466.5,-24 466.5,-12 466.5,-12 466.5,-6 472.5,-0 478.5,-0 478.5,-0 508.5,-0 508.5,-0 514.5,-0 520.5,-6 520.5,-12 520.5,-12 520.5,-24 520.5,-24 520.5,-30 514.5,-36 508.5,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"493.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">(...)</text>\n",
       "</g>\n",
       "<!-- 4555&#45;&gt;27823 -->\n",
       "<g id=\"edge7\" class=\"edge\"><title>4555&#45;&gt;27823</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M473.185,-71.7615C476.543,-63.0419 480.009,-54.0385 483.107,-45.9921\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"486.472,-46.9949 486.799,-36.4051 479.939,-44.4797 486.472,-46.9949\"/>\n",
       "</g>\n",
       "<!-- 28561 -->\n",
       "<g id=\"node10\" class=\"node\"><title>28561</title>\n",
       "<path fill=\"#f3c3a0\" stroke=\"black\" d=\"M884.5,-155C884.5,-155 656.5,-155 656.5,-155 650.5,-155 644.5,-149 644.5,-143 644.5,-143 644.5,-84 644.5,-84 644.5,-78 650.5,-72 656.5,-72 656.5,-72 884.5,-72 884.5,-72 890.5,-72 896.5,-78 896.5,-84 896.5,-84 896.5,-143 896.5,-143 896.5,-149 890.5,-155 884.5,-155\"/>\n",
       "<text text-anchor=\"start\" x=\"698\" y=\"-139.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Hillshade_Noon ≤ 0.768</text>\n",
       "<text text-anchor=\"start\" x=\"735\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.476</text>\n",
       "<text text-anchor=\"start\" x=\"714.5\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 146450</text>\n",
       "<text text-anchor=\"start\" x=\"652.5\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [94836, 47219, 0, 0, 0, 0, 4395]</text>\n",
       "<text text-anchor=\"start\" x=\"743\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n",
       "</g>\n",
       "<!-- 28560&#45;&gt;28561 -->\n",
       "<g id=\"edge9\" class=\"edge\"><title>28560&#45;&gt;28561</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M775.071,-190.907C774.572,-182.558 774.04,-173.671 773.523,-165.02\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"777.015,-164.794 772.924,-155.021 770.028,-165.212 777.015,-164.794\"/>\n",
       "</g>\n",
       "<!-- 43370 -->\n",
       "<g id=\"node13\" class=\"node\"><title>43370</title>\n",
       "<path fill=\"#f9e1d0\" stroke=\"black\" d=\"M1148,-155C1148,-155 927,-155 927,-155 921,-155 915,-149 915,-143 915,-143 915,-84 915,-84 915,-78 921,-72 927,-72 927,-72 1148,-72 1148,-72 1154,-72 1160,-78 1160,-84 1160,-84 1160,-143 1160,-143 1160,-149 1154,-155 1148,-155\"/>\n",
       "<text text-anchor=\"start\" x=\"957\" y=\"-139.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Wilderness_Area3 ≤ 0.129</text>\n",
       "<text text-anchor=\"start\" x=\"1002\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.535</text>\n",
       "<text text-anchor=\"start\" x=\"985\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 24444</text>\n",
       "<text text-anchor=\"start\" x=\"923\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [13337, 1189, 0, 0, 0, 0, 9918]</text>\n",
       "<text text-anchor=\"start\" x=\"1010\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n",
       "</g>\n",
       "<!-- 28560&#45;&gt;43370 -->\n",
       "<g id=\"edge12\" class=\"edge\"><title>28560&#45;&gt;43370</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M867.706,-190.907C890.494,-180.653 915.088,-169.585 938.294,-159.143\"/>\n",
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       "<!-- 28562 -->\n",
       "<g id=\"node11\" class=\"node\"><title>28562</title>\n",
       "<path fill=\"#c0c0c0\" stroke=\"black\" d=\"M749.5,-36C749.5,-36 719.5,-36 719.5,-36 713.5,-36 707.5,-30 707.5,-24 707.5,-24 707.5,-12 707.5,-12 707.5,-6 713.5,-0 719.5,-0 719.5,-0 749.5,-0 749.5,-0 755.5,-0 761.5,-6 761.5,-12 761.5,-12 761.5,-24 761.5,-24 761.5,-30 755.5,-36 749.5,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"734.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">(...)</text>\n",
       "</g>\n",
       "<!-- 28561&#45;&gt;28562 -->\n",
       "<g id=\"edge10\" class=\"edge\"><title>28561&#45;&gt;28562</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M754.815,-71.7615C751.457,-63.0419 747.991,-54.0385 744.893,-45.9921\"/>\n",
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       "<g id=\"node12\" class=\"node\"><title>39159</title>\n",
       "<path fill=\"#c0c0c0\" stroke=\"black\" d=\"M821.5,-36C821.5,-36 791.5,-36 791.5,-36 785.5,-36 779.5,-30 779.5,-24 779.5,-24 779.5,-12 779.5,-12 779.5,-6 785.5,-0 791.5,-0 791.5,-0 821.5,-0 821.5,-0 827.5,-0 833.5,-6 833.5,-12 833.5,-12 833.5,-24 833.5,-24 833.5,-30 827.5,-36 821.5,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"806.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">(...)</text>\n",
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       "<!-- 28561&#45;&gt;39159 -->\n",
       "<g id=\"edge11\" class=\"edge\"><title>28561&#45;&gt;39159</title>\n",
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       "<g id=\"node14\" class=\"node\"><title>43371</title>\n",
       "<path fill=\"#c0c0c0\" stroke=\"black\" d=\"M1016.5,-36C1016.5,-36 986.5,-36 986.5,-36 980.5,-36 974.5,-30 974.5,-24 974.5,-24 974.5,-12 974.5,-12 974.5,-6 980.5,-0 986.5,-0 986.5,-0 1016.5,-0 1016.5,-0 1022.5,-0 1028.5,-6 1028.5,-12 1028.5,-12 1028.5,-24 1028.5,-24 1028.5,-30 1022.5,-36 1016.5,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"1001.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">(...)</text>\n",
       "</g>\n",
       "<!-- 43370&#45;&gt;43371 -->\n",
       "<g id=\"edge13\" class=\"edge\"><title>43370&#45;&gt;43371</title>\n",
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       "<g id=\"node15\" class=\"node\"><title>44216</title>\n",
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       "<text text-anchor=\"middle\" x=\"1073.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">(...)</text>\n",
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       "<!-- 43370&#45;&gt;44216 -->\n",
       "<g id=\"edge14\" class=\"edge\"><title>43370&#45;&gt;44216</title>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7f14fa0be128>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "\n",
    "data = export_graphviz(trainedtree,out_file=None,feature_names= X.columns,\n",
    "                       class_names=['1', '2', '3', '4', '5', '6', '7'],  \n",
    "                       filled=True, rounded=True,  \n",
    "                       max_depth=2,\n",
    "                       special_characters=True)\n",
    "graph = graphviz.Source(data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1273.6673220040002\n",
      "[[59679  3564     2     0    17     5   132]\n",
      " [ 1896 82685   174     5   122    93    18]\n",
      " [    0   154 10415    40     6   207     0]\n",
      " [    0     0    84   714     0    31     0]\n",
      " [   38   625    32     0  2167     8     0]\n",
      " [    8   174   370    11     4  4682     0]\n",
      " [  282    39     0     0     0     0  5821]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.96      0.94      0.95     63399\n",
      "           2       0.95      0.97      0.96     84993\n",
      "           3       0.94      0.96      0.95     10822\n",
      "           4       0.93      0.86      0.89       829\n",
      "           5       0.94      0.76      0.84      2870\n",
      "           6       0.93      0.89      0.91      5249\n",
      "           7       0.97      0.95      0.96      6142\n",
      "\n",
      "    accuracy                           0.95    174304\n",
      "   macro avg       0.95      0.90      0.92    174304\n",
      "weighted avg       0.95      0.95      0.95    174304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train,Y_Train)\n",
    "print(time.process_time() - start)\n",
    "predictionforest = trainedforest.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many different methods which can be applied for Feature Selection. Some of the most important ones are:\n",
    "\n",
    "- Filter Method = filtering our dataset and taking only a subset of it containg all the relevant features (eg. correlation matrix using Pearson Correlation)\n",
    "- Wrapper Method = follows the same objective of the FIlter Method but uses a Machine Learning model as it's evaluation criteria (eg. Forward/Backward/Bidirectional/Recursive Feature Elimination). We feed some features to our Machine Learning model, evaluate their performance and then decide if add or remove feature to increase accuracy. As a result, this mothod can be more accurate than filtering, is more computationally expensive.\n",
    "- Embedded Method = like the FIlter Method also the Embedded Method makes use of a Machine Learning model. The difference between the two different methods is that the Embedded Method examines the different training iterations of our ML model and then ranks the importance of each feature based on how much each of the features contributed to the ML model training (eg. LASSO Regularization)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Trees models which are based on ensembles (eg. Extra Trees and Random Forest) can be used to rank the importaqnce of the different features. Knowing which features our model is giving most importance can be of vital importance to understand how our model is making it's predictions (therefore making it more explainable). At the same time, we can get rid of the features which do not bring any benefit to our model (our confuse it to make a wrong decision!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f14fa029eb8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pU84777xMnDgxgwcPzurVq4tjO3fuzNq1axutYCoUChk8eHDmzp2brVu3ZujQobstRX3zzTezefPmYsm2Zs2aYpbDDz8806dP32NJ5/upr6/Pyy+/XHx/3nnn5bzzzkuS/OEPf8h9992X7t27761LAwAAAJqYJaI0mpUrV2b27NlZvXp1GhoasmHDhjz88MPp1atXhg0blrlz5+b555/P9u3bc/vtt6dNmzbp2bNno+UZOXJk5s2bl8cffzwjRozYY3zGjBnZvn17li5dmkceeSRDhw5NkpxzzjmZMWNGXnnllSTJpk2b8sQTTyRJXn755Tz99NPZsmVL6uvr89RTT+Xf/u3fcsIJJyRJtm3blhUrVqShoSGvv/56rrrqqpx++uk56qijGu06AQAAgH3LDDYaTUVFRZYuXZo5c+bkrbfeSuvWrdOvX79MmzYtlZWVmTx5ciZPnpwNGzYUH3Swtx9w8Kd69OiRNm3apFmzZunatetuY4ceemgqKyszYMCAFAqFjB8/Pn369EmSjBo1Ks2bN8+YMWNSV1eXtm3bpn///jn99NPT0NCQO++8M5MnT06zZs3SqVOnXHPNNcWHHGzbti2TJ0/OK6+8klatWmXIkCG57LLLGu0aAQAAgH2vWcN7PeIQPqHGjh2bvn37ZvTo0cVt8+fPzxVXXJHa2tomTPb+li1blpEjR+aYQRPT7KA9f7utKXymZXkeumFIU8cAAACAveaP99/V1dXp1q3bRzrWElE+NZYvX54FCxbs8fRQAAAAgFJYIsoBo6amJlOnTt1je1lZWRYtWvSBx06ZMiVPPfVUpkyZknbt2jVWRAAAAOBTSMHGAWP48OEZPnz4xzr2xhtvfN+xE088cb9dHgoAAADs/ywRBQAAAIASKNgAAAAAoASWiMIBolXLsjRvuX/8yR7Uav/IAQAAAPsDd8lwgLhmzAkf+THBjWlH/a60KDcJFgAAANwdAx+Lcg0AAADe5Q4ZAAAAAEqgYAMAAACAEijYAAAAAKAECjYAAAAAKIGCDQAAAABKoGADAAAAgBIo2AAAAACgBAo2AAAAACiBgg0AAAAASqBgAwAAAIASKNgAAAAAoAQKNgAAAAAogYINAAAAAEqgYAMAAACAEijYAAAAAKAECjYAAAAAKIGCDQAAAABKoGADAAAAgBIo2AAAAACgBAo2AAAAACiBgg0AAAAASqBgAwAAAIASKNgAAAAAoAQKNgAAAAAogYINAAAAAEqgYAMAAACAEijYgI9tR/2upo4AAAAATa68qQMAH87UWb9I84NebOoYRQe1Ks89V5/W1DEAAACgySnY4ACxddvONCurb+oYAAAAwH9hiSgAAAAAlEDBBgAAAAAlULABAAAAQAkUbAAAAABQAgUbAAAAAJRAwQYAAAAAJVCwsV8aMmRI5s+fnySZOXNmJkyY0MSJAAAAAN5beVMH4JPtueeey80335wVK1YkSY488shceumlGThw4Ace99hjj33oc6xevTpDhgwpvt+yZUsKhULKysqSJNdcc02GDx/+MdJ/ON/85jfzm9/8Jlu3bk1lZWXGjBmTs846qzj+6KOP5gc/+EHWrl2bysrKfOtb38qwYcMaLQ8AAACwbynYaDRvv/12xo4dmyuvvDLDhg1LfX19lixZUiy+9paOHTtm8eLFxff9+/fPLbfckr59++7V87yfyZMn5/Of/3wKhUJeeOGFnH/++enSpUu6deuWV199NVdccUV+8IMfZMCAAVmwYEHGjBmTbt265fOf//w+yQcAAAA0LktEaTSrVq1KfX19RowYkfLy8rRq1Sp9+/ZNVVVVkqS6ujqnnXZaevfunVGjRmXVqlXFYwcNGpTa2tq9kqOuri7du3fPhg0bitteffXVdO/ePRs3bsz8+fMzYMCA3HXXXenXr18GDhyYe++9t7jvrl27Mnv27Jx66qnp06dPxo4dm7q6uuJ4ly5dUigUkiQNDQ1JkpdeeinJu7Pr2rZtm4EDB6ZZs2bp27dvjjjiiPz+97/fI+e6deuybNmyPV4rV67cK98DAAAA0DjMYKPRHH300SkUCpk0aVKGDh2anj17pl27dkmSZ599NtOnT8+sWbPStWvX3HHHHbnooovy6KOPpkWLFns1R/v27dOnT5/Mmzcvo0aNSpLU1NTk5JNPTtu2bZMk69evT11dXWpra7N8+fKMHj06xx13XKqqqjJnzpw8+eSTufvuu3PYYYfl1ltvzaRJk/KjH/2oeI4JEybkJz/5SbZt25auXbsWl8D26tUrnTp1ytNPP52TTz45v/zlL7Nx48Ycf/zxe+R88MEHc9ttt+3VawcAAAAan4KNRlNRUZH77rsvs2fPzrXXXpu1a9emd+/euf7661NTU5MRI0YUi6Zx48blvvvuy5IlS4oz3Pams846K7NmzdqtYLvssst222fixIkpFArp0aNHzjjjjNTU1KSqqir3339/rr766hxxxBFJkksuuSS9evVKXV1d2rdvnySZMWNG6uvrs3Dhwjz33HNp2bJlkqRFixYZMWJEJk2alG3btqV58+a54YYbcuihh+6R8eyzz86gQYP22L5y5co9sgIAAAD7DwUbjapz586ZPn16kneXZU6dOjWXX355KioqctJJJxX3Ky8vT4cOHbJu3bpGyXHKKadk2rRpWbFiRbZs2ZJNmzZlwIABxfFDDjkkrVu3Lr7v2LFjFi5cmCR57bXXMn78+DRv/p8rqsvKyrJ27dpiwfbHazjhhBMyd+7cPPTQQzn33HPz7//+77nllltyzz33pHv37nnhhRdy0UUXpW3btrudP0kqKytTWVnZKNcPAAAANB4FG/tMp06dct5552XixIkZPHhwVq9eXRzbuXNn8SmbjaFQKGTw4MGZO3dutm7dmqFDh+62FPXNN9/M5s2biyXbmjVrilkOP/zwTJ8+/UPPrKuvr8/LL7+cJFmxYkW+9KUvpWfPnkmSY489Nn/913+dn/3sZ3sUbAAAAMCByUMOaDQrV67M7Nmzs3r16jQ0NGTDhg15+OGH06tXrwwbNixz587N888/n+3bt+f2229PmzZtikVUYxg5cmTmzZuXxx9/PCNGjNhjfMaMGdm+fXuWLl2aRx55JEOHDk2SnHPOOZkxY0ZeeeWVJMmmTZvyxBNPJElefvnlPP3009myZUvq6+vz1FNP5d/+7d9ywgknJEl69OiRX/3qV1m2bFnxO6mtrU2XLl0a7ToBAACAfcsMNhpNRUVFli5dmjlz5uStt95K69at069fv0ybNi2VlZWZPHlyJk+enA0bNhQfdLC3H3Dwp3r06JE2bdqkWbNm6dq1625jhx56aCorKzNgwIAUCoWMHz8+ffr0SZKMGjUqzZs3z5gxY1JXV5e2bdumf//+Of3009PQ0JA777wzkydPTrNmzdKpU6dcc801xYcc9OvXL5dcckkmTJiQ119/PW3atMmZZ56ZkSNHNtp1AgAAAPtWs4aGhoamDgH7ytixY9O3b9+MHj26uG3+/Pm54oorUltb24TJ3t+yZcsycuTIHDNoYpod1KGp4xR9pmV5HrphSFPHAAAAgL3ij/ff1dXV6dat20c61hJRPjWWL1+eBQsW5Mwzz2zqKAAAAMAniCWiHDBqamoyderUPbaXlZVl0aJFH3jslClT8tRTT2XKlClp165dY0UEAAAAPoUUbBwwhg8fnuHDh3+sY2+88cb3HTvxxBP32+WhAAAAwP7PElEAAAAAKIGCDQAAAABKoGADAAAAgBL4DTY4QLRqWZbmLfefP9mDWu0/WQAAAKApuUOGA8Q1Y05It27dmjrGbnbU70qLchNhAQAA+HRzZwx8bMo1AAAAULABAAAAQEkUbAAAAABQAgUbAAAAAJRAwQYAAAAAJVCwAQAAAEAJFGwAAAAAUAIFGwAAAACUQMEGAAAAACVQsAEAAABACRRsAAAAAFACBRsAAAAAlEDBBgAAAAAlULABAAAAQAkUbAAAAABQAgUbAAAAAJRAwQYAAAAAJVCwAQAAAEAJFGwAAAAAUAIFGwAAAACUQMEGAAAAACVQsAEAAABACRRsAAAAAFACBRsAAAAAlEDBBgAAAAAlULABAAAAQAkUbEBJdtTvauoIAAAA0KTKmzoA8OFMnfWLND/oxaaOsZuDWpXnnqtPa+oYAAAA0KQUbHCA2LptZ5qV1Td1DAAAAOC/sEQUAAAAAEqgYAMAAACAEijYAAAAAKAECjYAAAAAKIGCDQAAAABKoGADAAAAgBIo2NgvDRkyJPPnz0+SzJw5MxMmTGjiRAAAAADvrbypA/DJ9txzz+Xmm2/OihUrkiRHHnlkLr300gwcOPADj3vsscc+9DlWr16dIUOGFN9v2bIlhUIhZWVlSZJrrrkmw4cP/xjp/7zXX38906dPz8KFC7N58+YcffTRmTRpUk488cQkyYsvvpjLLrssL730Unbt2pW/+qu/ypQpU9KrV69GyQMAAADsewo2Gs3bb7+dsWPH5sorr8ywYcNSX1+fJUuWFIuvvaVjx45ZvHhx8X3//v1zyy23pG/fvnv1PO/lnXfeSffu3TNlypQcdthh+dd//deMGzcujz/+eA4//PAceuih+f73v5+//Mu/TJL867/+ay666KL8/Oc/T3m5Pz8AAAD4JLBElEazatWq1NfXZ8SIESkvL0+rVq3St2/fVFVVJUmqq6tz2mmnpXfv3hk1alRWrVpVPHbQoEGpra3dKznq6urSvXv3bNiwobjt1VdfTffu3bNx48bMnz8/AwYMyF133ZV+/fpl4MCBuffee4v77tq1K7Nnz86pp56aPn36ZOzYsamrq0uSHHXUUbnwwgvTvn37NG/ePKeffnoqKyuzbNmyJElFRUWOPPLINGvWLA0NDWnWrFk2btyYTZs27ZVrAwAAAJqego1Gc/TRR6dQKGTSpEn56U9/mjfeeKM49uyzz2b69Om58cYb8/Of/zy9evXKRRddlB07duz1HO3bt0+fPn0yb9684raampqcfPLJadu2bZJk/fr1qaurS21tbWbOnJkZM2Zk0aJFSZI5c+bkySefzN13351nnnkmxxxzTCZNmvSe51qzZk1effXV/NVf/dVu2wcMGJDu3bvnkksuyd/8zd/ks5/97B7Hrlu3LsuWLdvjtXLlyr31VQAAAACNwBo1Gk1FRUXuu+++zJ49O9dee23Wrl2b3r175/rrr09NTU1GjBiR448/Pkkybty43HfffVmyZElxhtvedNZZZ2XWrFkZNWpUkncLtssuu2y3fSZOnJhCoZAePXrkjDPOSE1NTaqqqnL//ffn6quvzhFHHJEkueSSS9KrV6/U1dWlffv2xeO3bduWCRMm5JxzzsnnPve53T67trY2W7duzaOPPpoWLVq8Z8YHH3wwt9122967aAAAAGCfULDRqDp37pzp06cneXdZ5tSpU3P55ZenoqIiJ510UnG/8vLydOjQIevWrWuUHKecckqmTZuWFStWZMuWLdm0aVMGDBhQHD/kkEPSunXr4vuOHTtm4cKFSZLXXnst48ePT/Pm/znhs6ysLGvXri0WbNu3b8+3vvWtdOjQId/5znfeM0OrVq3y9a9/PYMGDUrXrl1zzDHH7DZ+9tlnZ9CgQXsct3Llyj3KQAAAAGD/oWBjn+nUqVPOO++8TJw4MYMHD87q1auLYzt37szatWtTWVnZKOcuFAoZPHhw5s6dm61bt2bo0KG7zSR78803s3nz5mLJtmbNmmKWww8/PNOnT3/fmXXbt2/P+PHjUygUcvPNN//ZhzjU19fnlVde2aNgq6ysbLTrBwAAABqP32Cj0axcuTKzZ8/O6tWr09DQkA0bNuThhx9Or169MmzYsMydOzfPP/98tm/fnttvvz1t2rRJz549Gy3PyJEjM2/evDz++OMZMWLEHuMzZszI9u3bs3Tp0jzyyCMZOnRokuScc87JjBkz8sorryRJNm3alCeeeCLJu+XaJZdckoaGhsyYMWOPJ4P+4he/yPPPP5/6+vq88847ufXWW/P222+ne/fujXadAAAAwL5lBhuNpqKiIkuXLs2cOXPy1ltvpXXr1unXr1+mTZuWysrKTJ48OZMnT86GDRvStWvX3HHHHe/7+2QOkEjGAAAgAElEQVR7Q48ePdKmTZs0a9YsXbt23W3s0EMPTWVlZQYMGJBCoZDx48enT58+SZJRo0alefPmGTNmTOrq6tK2bdv0798/p59+en71q1/lpz/9afEJqX/0ve99L4MHD87mzZtz3XXXZc2aNSkUCunSpUtmz56dww47rNGuEwAAANi3mjU0NDQ0dQjYV8aOHZu+fftm9OjRxW3z58/PFVdckdra2iZM9v6WLVuWkSNH5phBE9PsoA5NHWc3n2lZnoduGNLUMQAAAKBkf7z/rq6uTrdu3T7SsZaI8qmxfPnyLFiwIGeeeWZTRwEAAAA+QSwR5YBRU1OTqVOn7rG9rKwsixYt+sBjp0yZkqeeeipTpkxJu3btGisiAAAA8CmkYOOAMXz48AwfPvxjHXvjjTe+79iJJ5643y4PBQAAAPZ/logCAAAAQAkUbAAAAABQAgUbAAAAAJTAb7DBAaJVy7I0b7l//cke1Gr/ygMAAABNwd0xHCCuGXNCunXr1tQx9rCjfldalJsMCwAAwKeXu2KgJMo1AAAAPu3cGQMAAABACRRsAAAAAFACBRsAAAAAlEDBBgAAAAAlULABAAAAQAkUbAAAAABQAgUbAAAAAJRAwQYAAAAAJVCwAQAAAEAJFGwAAAAAUAIFGwAAAACUQMEGAAAAACVQsAEAAABACRRsAAAAAFACBRsAAAAAlEDBBgAAAAAlULABAAAAQAkUbAAAAABQAgUbAAAAAJRAwQYAAAAAJVCwAQAAAEAJFGwAAAAAUAIFGwAAAACUQMEGAAAAACVQsAEAAABACRRswF6zo35XU0cAAACAfa68qQMAH87UWb9I84NebOoY7+ugVuW55+rTmjoGAAAA7HMKNjhAbN22M83K6ps6BgAAAPBfWCIKAAAAACVQsAEAAABACRRsAAAAAFACBRsAAAAAlEDBBgAAAAAlULABAAAAQAkUbAAAAABQAgUbAAAAAJRAwfYp9c1vfjMPP/xwkqS6ujp/8zd/8777TpgwITNnztxX0QAAAAAOKAq2T4g777wz559//m7bzjnnnJx++um7bZswYUJuuOGGzJ49O9/4xjf2ZcT9xv/9v/83xx13XO69995GP9c//uM/ZtiwYenatWtuvvnmRj8fAAAAsO8p2D4h+vTpkyVLlmT79u1Jkm3btuX3v/99tmzZkvXr1xf3W7hwYfr06bNPMjU0NKS+vn6fnOujqK6uTps2bfLjH//4A/fbsWNHyec66qijMnny5AwaNOjP7rtu3bosW7Zsj9fKlStLzgEAAAA0nvKmDsDe0b1795SVleXXv/51qqqqsnjx4nTr1i2f/exns3Dhwpx++ulZtWpV1q9fn6qqqpx//vkZPHhwzjnnnD0+65e//GWuu+66rF69Ol/96lf3KJp+9rOfZcaMGXn55Zfzl3/5l7nqqqtSVVWVJDn//PNz/PHHZ/HixXn++efzz//8z3nggQfSqlWrrF+/Pj//+c/TqVOn/MM//EO+8IUvJElef/31fO9738uCBQvSokWLfP3rX8+4cePSvHnzvPTSS/mf//N/ZtmyZWnRokW+9KUv5fbbb09DQ0P+/u//PjU1Ndm+fXsOP/zw3HjjjenWrdsHfk/r169PbW1tvv/97+fb3/52fvvb3+a4445Lkrz66qv5yle+ku9973u5/fbb85nPfCaPPfZYXnzxxVx//fV5/vnnc/DBB+fCCy/M3/7t3yZJnn/++Xzve9/LCy+8kJYtW+arX/1qrrzyyhQKhSTJiBEjkiRPPPHEn/0/fPDBB3Pbbbf92f0AAACA/YsZbJ8Q5eXl6dWrVxYsWJAkWbRoUaqqqtK7d+8sXLgwybuz14499ti0bdv2fT9n48aNufjiizNmzJgsXLgwJ5xwQp5++uni+PLlyzN58uRceeWVWbBgQS699NKMGzcub7zxRnGff/mXf8lVV12V5557Ll27dk2SPPbYYxk9enQWLVqUfv365frrr0+S7Nq1K3/3d3+Xo446Kj/96U/z0EMP5Sc/+Un+z//5P0mS//W//lc6d+6cBQsW5Gc/+1lGjx6dJHnmmWfy5JNPpqamJosWLcrMmTPz2c9+9s9+TzU1NenUqVO+9rWvpaqqKtXV1Xvs88wzz6SmpiY//vGPs2XLllx44YUZNGhQnnnmmdx1112ZNWtWfv7znydJysrKcvnll+eXv/xlHnzwwTz77LP53//7f//ZHO/l7LPPTnV19R6vm2666WN9HgAAALBvKNg+Qfr06VMs0xYsWJDevXunqqpqt21/bnnov//7v+dzn/tczjzzzJSXl2fEiBE59thji+MPPPBAvv71r6eqqirNmzfPySefnC5duqS2tra4z5lnnpkvfOELKSsrK87k+spXvpIvfvGLKSsry5lnnpnf/OY3SZKlS5dm7dq1+fa3v52WLVumffv2GTVqVB599NEk7xaHr7/+etasWZNCoVCcKVdeXp5t27blhRdeyM6dO3P00UenQ4cOf/Y7+pd/+ZcMGzYsSXLGGWekpqZmjxl648ePT0VFRVq1apWf/vSnOfTQQ3PuueemRYsW+fznP59vfOMbxXxdu3bNF7/4xZSXl6dTp045++yzi9/3R1VZWZlu3brt8ercufPH+jwAAABg37BE9BOkd+/e+ad/+qe88847Wb58eXr27JlCoZA//OEP+cMf/pCFCxfmqquu+sDPWLduXQ4//PDdtnXs2LH479deey0LFizIgw8+WNxWX1+f/v37F9//1+OT5NBDDy3+u1WrVnnnnXeSvLss84033kjv3r2L47t27Sp+xmWXXZZbb70155xzTlq3bp0LLrggf/u3f5sTTjghF198cW666aa88sorGTRoUL7zne984Oy8X//611mxYkV+8IMfJEm+9rWv5brrrst//Md/5JRTTnnf6/1//+//FYu9JNm5c2fx/apVq/L3f//3Wbp0abZs2ZKdO3emS5cu75sBAAAA+ORRsH2C9OjRI0ly//3355hjjinOHvviF7+Y6urq1NXV7VYUvZfKysqsWbNmt21r1qwp/l7a4Ycfnm9+85sZP378+35G8+YffmJkx44d06FDh92Wof6pww47LNddd12SZPHixfnv//2/p3fv3jn22GNzwQUX5IILLsgbb7yRCRMm5M4778yUKVPe91zV1dVp1qzZbr87t3Pnzvz4xz/erWD70/wdO3ZMr1693nfZ57Rp03Lsscfm+9//fioqKvLP//zPeeyxxz709QMAAAAHPktEP0FatGiR448/Pj/84Q93K9J69+6dH/7whznmmGPSrl27D/yMgQMH5sUXX8y8efNSX1+fuXPnZsWKFcXxs88+Ow8++GAWLVqUXbt2ZevWrfnlL3+ZtWvXfqzM3bt3z1/8xV/kBz/4Qd55553s2rUrL774YvG35J544oniZ1dUVKRZs2bFhzksWbIkO3bsSKtWrVIoFFJWVva+59m2bVsef/zxfPe7383cuXOLr1tvvTW1tbW7PWn1T5188sl57bXX8vDDD2f79u2pr6/Pb3/72/z6179OkmzevDkVFRVp3bp1Vq1alQceeGC343fs2JFt27Zl586d2blzZ7Zt27ZfPlkVAAAA+PgUbJ8wffr0yfr163dbctm7d++sX7/+z/7+WpL8xV/8RW677bbccccd6d27d+bPn58vf/nLxfFu3brlxhtvzE033ZS+ffvmy1/+cu6+++7s2rXrY+UtKyvLP/3TP+Wll17Kqaeemt69e+fb3/52Xn/99STv/kbbN77xjfTq1Stjx47N5Zdfns6dO2fz5s357ne/mz59+uTLX/5yKioqMnbs2Pc9z1NPPZVmzZrl61//eg477LDi6ytf+Uo+97nPpaam5j2Pa926dX74wx/m6aefzsCBA3PCCSfk6quvzubNm5MkU6ZMyRNPPJEvfvGLueqqq/K1r31tt+O/+93vpkePHqmpqckPf/jD9OjRI3fcccfH+q4AAACA/VOzhoaGhqYOAby/ZcuWZeTIkTlm0MQ0O+jPP8ihqXymZXkeumFIU8cAAACAj+WP99/V1dXp1q3bRzrWDDYAAAAAKIGHHPCJMmTIkKxevXqP7RdffHHGjBnTBIkAAACATzoFG58onuAJAAAA7GuWiAIAAABACcxggwNEq5Zlad5y//2TPajV/psNAAAAGpM7YjhAXDPmhI/8FJN9bUf9rrQoNzEWAACATxd3wsBeo1wDAADg08jdMAAAAACUQMEGAAAAACVQsAEAAABACRRsAAAAAFACBRsAAAAAlEDBBgAAAAAlULABAAAAQAkUbAAAAABQAgUbAAAAAJRAwQYAAAAAJVCwAQAAAEAJFGwAAAAAUAIFGwAAAACUQMEGAAAAACVQsAEAAABACRRsAAAAAFACBRsAAAAAlEDBBgAAAAAlULABAAAAQAkUbAAAAABQAgUbAAAAAJRAwQYAAAAAJVCwAQAAAEAJFGwAAAAAUAIFGwAAAACUQMEGAAAAACVQsAF71Y76XU0dAQAAAPap8qYOAHw4U2f9Is0PerGpY3ygg1qV556rT2vqGAAAALBPKdjgALF12840K6tv6hgAAADAf2GJKAAAAACUQMEGAAAAACVQsAEAAABACRRsAAAAAFACBRsAAAAAlEDBBgAAAAAlULCxXxoyZEjmz5+fJJk5c2YmTJjQxIkAAAAA3puCjUb13HPP5dxzz01VVVWqqqoycuTI/Md//MefPe6xxx7LiSee+KHOsXr16vTq1av46tKlS3r06FF8X1NTU+plfKBbbrklQ4cOTdeuXTNjxow9xn/xi19kyJAh6dmzZ/7bf/tvWblyZaPmAQAAAPat8qYOwCfX22+/nbFjx+bKK6/MsGHDUl9fnyVLlqSsrGyvnqdjx45ZvHhx8X3//v1zyy23pG/fvnv1PO/nqKOOymWXXZYHHnhgj7E33ngj3/rWt3Lttdfmq1/9ambNmpWLL744jz/++F7/HgAAAICmYQYbjWbVqlWpr6/PiBEjUl5enlatWqVv376pqqpKklRXV+e0005L7969M2rUqKxatap47KBBg1JbW7tXctTV1aV79+7ZsGFDcdurr76a7t27Z+PGjZk/f34GDBiQu+66K/369cvAgQNz7733FvfdtWtXZs+enVNPPTV9+vTJ2LFjU1dXVxw/66yzMnDgwLRu3XqPcz/55JM55phjMmTIkBQKhYwdOzYbN27Mr371qz32XbduXZYtW7bHy4w3AAAA2L+ZwUajOfroo1MoFDJp0qQMHTo0PXv2TLt27ZIkzz77bKZPn55Zs2ala9euueOOO3LRRRfl0UcfTYsWLfZqjvbt26dPnz6ZN29eRo0alSSpqanJySefnLZt2yZJ1q9fn7q6utTW1mb58uUZPXp0jjvuuFRVVWXOnDl58sknc/fdd+ewww7LrbfemkmTJuVHP/rRnz337373uxx33HHF9+Xl5TnmmGPyu9/9Ln369Nlt3wcffDC33Xbb3rtwAAAAYJ8wg41GU1FRkfvuuy+FQiHXXntt+vfvnwsuuCAvv/xyampqMmLEiBx//PEpFAoZN25cNm7cmCVLljRKlrPOOiuPPPJI8X1NTU3OPPPM3faZOHFiCoVCevTokTPOOKP42233339/JkyYkCOOOCKFQiGXXHJJFi9evNsstvfzzjvv5OCDD95t28EHH5zNmzfvse/ZZ5+d6urqPV433XTTx7lkAAAAYB8xg41G1blz50yfPj3Ju8syp06dmssvvzwVFRU56aSTivuVl5enQ4cOWbduXaPkOOWUUzJt2rSsWLEiW7ZsyaZNmzJgwIDi+CGHHLLbEs+OHTtm4cKFSZLXXnst48ePT/Pm/9lHl5WVZe3atWnfvv0Hnveggw7K22+/vdu2t99++z2Xk1ZWVqaysvJjXR8AAADQdBRs7DOdOnXKeeedl4kTJ2bw4MFZvXp1cWznzp1Zu3ZtoxVMhUIhgwcPzty5c7N169YMHTp0t6Wob775ZjZv3lwsvtasWVPMcvjhh2f69OnF3477KI455pjdnmK6c+fO/O53v8v48eNLvCIAAABgf2GJKI1m5cqVmT17dlavXp2GhoZs2LAhDz/8cHr16pVhw4Zl7ty5ef7557N9+/bcfvvtadOmTXr27NloeUaOHJl58+bl8ccfz4gRI/YYnzFjRrZv356lS5fmkUceydChQ5Mk55xzTmbMmJFXXnklSbJp06Y88cQTxeN27NiRbdu2paGhIbt27cq2bduyc+fOJMlpp52W3/3ud3niiSeyffv23HXXXWnTpk2+9KUvNdp1AgAAAPuWGWw0moqKiixdujRz5szJW2+9ldatW6dfv36ZNm1aKisrM3ny5EyePDkbNmwoPuhgbz/g4E/16NEjbdq0SbNmzdK1a9fdxg499NBUVlZmwIABKRQKGT9+fPEhBKNGjUrz5s0zZsyY1NXVpW3btunfv39OP/30JMl3vvOdzJs3r/hZd911Vy699NJcfPHF+exnP5vbbrst1113XaZMmZIvfOELuf3221NWVtZo1wkAAADsW80aGhoamjoE7Ctjx45N3759M3r06OK2+fPn54orrkhtbW0TJnt/y5Yty8iRI3PMoIlpdlCHpo7zgT7TsjwP3TCkqWMAAADAR/bH++/q6up069btIx1riSifGsuXL8+CBQv2eHooAAAAQCksEeWAUVNTk6lTp+6xvaysLIsWLfrAY6dMmZKnnnoqU6ZMSbt27RorIgAAAPAppGDjgDF8+PAMHz78Yx174403vu/YiSeeuN8uDwUAAAD2f5aIAgAAAEAJFGwAAAAAUAIFGwAAAACUwG+wwQGiVcuyNG+5f//JHtRq/84HAAAAjcHdMBwgrhlzQrp169bUMf6sHfW70qLc5FgAAAA+PdwFA3uVcg0AAIBPG3fCAAAAAFACBRsAAAAAlEDBBgAAAAAlULABAAAAQAkUbAAAAABQAgUbAAAAAJRAwQb/n707DdKquvcF/INuGgKdqCgNEo/Rw3WIFArY3QhEUfQ6IQpEQzBC1IhEFBUnjBpBE0WPGk/EIQ5lEnOMErWFdjz3OAUJUeDqRcEodRAcGMUpEZma5n7w5r0hiKgv0IDPU7Wreu+1h//e8H7Yv1prLwAAAIAiCNgAAAAAoAgCNgAAAAAogoANAAAAAIogYAMAAACAIgjYAAAAAKAIAjYAAAAAKIKADQAAAACKIGADAAAAgCII2AAAAACgCAI2AAAAACiCgA0AAAAAiiBgAwAAAIAiCNgAAAAAoAgCNgAAAAAogoANAAAAAIogYAMAAACAIgjYAAAAAKAIAjYAAAAAKIKADdjgVtbVN3QJAAAAsMmUNnQBwOcz8vY/p3HzOQ1dxno1b1aa31x6WEOXAQAAAJuMgA22EMuWr0qjkrqGLgMAAAD4J4aIAgAAAEARBGwAAAAAUAQBGwAAAAAUQcAGAAAAAEUQsAEAAABAEQRsAAAAAFAEARsAAAAAFEHA9hV1yimn5L777kuS1NTU5Hvf+9469x0+fHjGjBmzqUoDAAAA2KII2LYSt956awYOHLjGtgEDBuSII45YY9vw4cNz5ZVX5o477shxxx23KUvcbPzXf/1X9thjj9x9990b/VpDhgxJt27d0rlz5xx22GGFUBMAAADYegjYthLV1dWZNm1aVqxYkSRZvnx5Xn/99SxdujSLFy8u7DdlypRUV1dvkppWr16durq6TXKtL6KmpibbbLNNHnjggc/cb+XKlUVf65xzzskzzzyTF154ITfffHP+/d//PS+99FLR5wUAAAA2HwK2rUSHDh1SUlJSCG9efPHFtG/fPlVVVZkyZUqSZPbs2Vm8eHEqKyszcODA3HPPPZ96rueeey69evVKp06dcsEFF6wVND377LPp169fKisr07dv30ydOrXQNnDgwFx33XU54YQT0rFjx0yfPj0XXnhhRo0alTPOOCOdOnVK796985e//KVwzDvvvJOzzz473bp1S48ePTJmzJjU19cnSd54440MHDgwnTt3TpcuXTJ06NAkn4R3o0ePTteuXbPvvvvmqKOOyowZM9b7nBYvXpwJEybk8ssvzyuvvJLXXnut0Pb2229njz32yP3335+ePXumT58+SZI5c+bklFNOSZcuXXLIIYes0fPt5Zdfzve///1UVlame/fuGTVqVCHkTJI99tgjZWVla9TwxhtvfGptixYtyowZM9ZaZs2atd77AgAAABqOgG0rUVpamk6dOmXy5MlJkqlTp6aysnKNgG3KlCnZfffds+22267zPB988EGGDh2awYMHZ8qUKenatWueeuqpQvurr76a8847LxdddFEmT56cs846K6effnree++9wj4PPvhgLr744rzwwgvZa6+9kiSPPPJITj755EydOjX77bdffv7znydJ6uvrc9ppp+Vb3/pWnn766fzhD3/Ik08+mfvvvz9J8stf/jLt2rXL5MmT8+yzz+bkk09OkkycODGPP/54amtrM3Xq1IwZMybbb7/9ep9TbW1tdtpppxx++OGprKxMTU3NWvtMnDgxtbW1eeCBB7J06dKcdNJJ6dmzZyZOnJjbbrstt99+e/70pz8lSUpKSnLBBRfkueeey9ixY/P888/nd7/73RrnO/fcc7P33nvnyCOPzA477JCDDjroU2sbO3Zs+vXrt9Zy/vnnr/e+AAAAgIYjYNuKVFdXF8K0yZMnp6qqKpWVlWtsW9/w0GeeeSa77LJL+vTpk9LS0vTt2ze77757of3ee+/Nsccem8rKyjRu3DgHHnhg9txzz0yYMKGwT58+ffLtb387JSUlhd5bBx98cDp37pySkpL06dMnr7zySpJk+vTpWbBgQc4+++w0bdo0rVu3zoknnpiHH344ySfB4TvvvJP58+enrKwslZWVhe3Lly/Pf//3f2fVqlXZdddd06ZNm/U+owcffDC9e/dOkhxzzDGpra1dq4fesGHDUl5enmbNmuXpp5/ODjvskOOPPz5NmjTJv/7rv+a4444r1LfXXnulc+fOKS0tzU477ZT+/fsXnvffXXfddXnxxRfz29/+Nv/zf/7PNG3a9FNr69+/f2pqatZarrnmmvXeFwAAANBwShu6ADacqqqq/OpXv8rHH3+cV199Nfvss0/Kysry/vvv5/3338+UKVNy8cUXf+Y5Fi1alB133HGNbW3bti38PXfu3EyePDljx44tbKurq0v37t0L6/98fJLssMMOhb+bNWuWjz/+OMknwzLfe++9VFVVFdrr6+sL5zj//PNzww03ZMCAAWnRokUGDRqUH/zgB+natWuGDh2aa665Jm+99VZ69uyZn/zkJ5/ZO++ll17KzJkzc9NNNyVJDj/88PzsZz/LH//4xxxyyCHrvN+//OUvhWAvSVatWlVYnz17dq666qpMnz49S5cuzapVq7Lnnnuude2SkpLst99+qa2tzb333rvWhBRJUlFRkYqKinXWDwAAAGyeBGxbkb333jtJcs8992S33XYr9B7r3LlzampqsnDhwjWCok9TUVGR+fPnr7Ft/vz5+fa3v53kk/DslFNOybBhw9Z5jsaNP3/HyLZt26ZNmzZrDEP9R61atcrPfvazJJ98V+6HP/xhqqqqsvvuu2fQoEEZNGhQ3nvvvQwfPjy33nprRowYsc5r1dTUpFGjRhkwYEBh26pVq/LAAw+sEbD9Y/1t27ZNp06d1hr2+XejRo3K7rvvnuuuuy7l5eX57W9/m0ceeWSdNaxatWqd32ADAAAAtkyGiG5FmjRpko4dO+bOO+9cI0irqqrKnXfemd122y0tW7b8zHP06NEjc+bMyUMPPZS6urqMGzcuM2fOLLT3798/Y8eOzdSpU1NfX59ly5blueeey4IFC75UzR06dMh2222Xm266KR9//HHq6+szZ86cwrfkHnvsscK5y8vL06hRo8JkDtOmTcvKlSvTrFmzlJWVpaSkZJ3XWb58eR599NH89Kc/zbhx4wrLDTfckAkTJqwx0+o/OvDAAzN37tzcd999WbFiRerq6vLaa68VJpNYsmRJysvL06JFi8yePTv33ntv4di33norTz75ZJYuXZq6uro8/fTTeeyxx9K1a9cv9awAAACAzZOAbStTXV2dxYsXrzHksqqqKosXL17v99eSZLvttsuNN96YW265JVVVVZk0adIaH+Vv3759rr766lxzzTXp0qVLDjrooPz6178uzPr5RZWUlORXv/pV3njjjRx66KGpqqrK2WefnXfeeSfJJ99oO+6449KpU6cMGTIkF1xwQdq1a5clS5bkpz/9aaqrq3PQQQelvLw8Q4YMWed1nnjiiTRq1CjHHntsWrVqVVgOPvjg7LLLLqmtrf3U41q0aJE777wzTz31VHr06JGuXbvm0ksvzZIlS5IkI0aMyGOPPZbOnTvn4osvzuGHH77G8bfffnu+853vpEuXLrn++utz6aWX5uCDD/5SzwoAAADYPDVavXr16oYuAli3GTNmpF+/ftmt5zlp1Hz9Ezk0tK81Lc0fruzV0GUAAADAF/L39++ampq0b9/+Cx2rBxsAAAAAFMEkB2xVevXqlXnz5q21fejQoRk8eHADVAQAAABs7QRsbFU+awZPAAAAgI3BEFEAAAAAKIKADQAAAACKYIgobCGaNS1J46ab/0+2ebPNv0YAAADYkLwJwxbissFdv/A0wQ1lZV19mpTqIAsAAMBXgzdgYIMTrgEAAPBV4i0YAAAAAIogYAMAAACAIgjYAAAAAKAIAjYAAAAAKIKADQAAAACKIGADAAAAgCII2AAAAACgCAI2AAAAACiCgA0AAAAAiiBgAwAAAIAiCNgAAAAAoAgCNgAAAAAogoANAAAAAIogYAMAAACAIgjYAAAAAKAIAjYAAAAAKIKADQAAAACKIGADAAAAgCII2AAAAACgCAI2AAAAACiCgA0AAAAAiiBgA41kHzIAACAASURBVAAAAIAiCNgAAAAAoAgCNgAAAAAogoANAAAAAIogYAM2qpV19Q1dAgAAAGxUpQ1dAPD5jLz9z2ncfE5Dl/GFNG9Wmt9celhDlwEAAAAblYANthDLlq9Ko5K6hi4DAAAA+CeGiAIAAABAEQRsAAAAAFAEARsAAAAAFEHABgAAAABFELABAAAAQBEEbAAAAABQBAEbAAAAABRBwMYWqVevXpk0aVKSZMyYMRk+fHgDVwQAAAB8VZU2dAF8tb3wwgu59tprM3PmzCTJzjvvnLPOOis9evT4zOMeeeSRz32NefPmpVevXoX1pUuXpqysLCUlJUmSyy67LEcfffSXqP6LmTVrVo455pgcfvjhufbaazf69QAAAIBNQ8BGg/noo48yZMiQXHTRRendu3fq6uoybdq0QvC1obRt2zYvvvhiYb179+75xS9+kS5dumzQ63yW1atXZ+TIkenYseMmuyYAAACwaRgiSoOZPXt26urq0rdv35SWlqZZs2bp0qVLKisrkyQ1NTU57LDDUlVVlRNPPDGzZ88uHNuzZ89MmDBhg9SxcOHCdOjQIe+++25h29tvv50OHTrkgw8+yKRJk3LAAQfktttuy3777ZcePXrk7rvvLuxbX1+fO+64I4ceemiqq6szZMiQLFy4cI1r3H///dlpp52y7777rrOORYsWZcaMGWsts2bN2iD3CQAAAGwcerDRYHbdddeUlZXl3HPPzVFHHZV99tknLVu2TJI8//zzGT16dG6//fbstddeueWWW/LjH/84Dz/8cJo0abJB62jdunWqq6vz0EMP5cQTT0yS1NbW5sADD8y2226bJFm8eHEWLlyYCRMm5NVXX83JJ5+cPfbYI5WVlbnrrrvy+OOP59e//nVatWqVG264Ieeee27+4z/+I0ny3nvv5bbbbsu9996bu+66a511jB07NjfeeOMGvTcAAABg49ODjQZTXl6e3//+9ykrK8vll1+e7t27Z9CgQXnzzTdTW1ubvn37pmPHjikrK8vpp5+eDz74INOmTdsotXz3u9/N+PHjC+u1tbXp06fPGvucc845KSsry957751jjjkmtbW1SZJ77rknw4cPzze/+c2UlZXlzDPPzIsvvljoxXbllVfmpJNOyvbbb/+ZNfTv3z81NTVrLddcc80GvlsAAABgQ9KDjQbVrl27jB49OsknwzJHjhyZCy64IOXl5dl///0L+5WWlqZNmzZZtGjRRqnjkEMOyahRozJz5swsXbo0H374YQ444IBC+ze+8Y20aNGisN62bdtMmTIlSTJ37twMGzYsjRv//7y6pKQkCxYsyOuvv57Zs2fn3/7t39ZbQ0VFRSoqKjbgXQEAAACbgoCNzcZOO+2UE044Ieecc06OPPLIzJs3r9C2atWqLFiwYKMFUGVlZTnyyCMzbty4LFu2LEcdddQaQ1H/+te/ZsmSJYWQbf78+YVadtxxx4wePbrw7bh/dP3112fWrFnp2rVrkmTZsmWpr6/Pyy+/nP/8z//cKPcCAAAAbFqGiNJgZs2alTvuuCPz5s3L6tWr8+677+a+++5Lp06d0rt374wbNy4vv/xyVqxYkZtvvjnbbLNN9tlnn41WT79+/fLQQw/l0UcfTd++fddqv/7667NixYpMnz4948ePz1FHHZUkGTBgQK6//vq89dZbSZIPP/wwjz32WJJk8ODBefzxxzN+/PiMHz8+xx13XHr06JHf/e53G+0+AAAAgE1LDzYaTHl5eaZPn5677rorf/vb39KiRYvst99+GTVqVCoqKnLeeeflvPPOy7vvvluY6GBDT3Dwj/bee+9ss802adSoUfbaa6812nbYYYdUVFTkgAMOSFlZWYYNG5bq6uokyYknnpjGjRtn8ODBWbhwYbbddtt07949RxxxRMrLy1NeXl44T4sWLdKsWTNDQQEAAGAr0mj16tWrG7oI2FwMGTIkXbp0ycknn1zYNmnSpFx44YWZMGFCg9Q0Y8aM9OvXL7v1PCeNmrdpkBq+rK81Lc0fruzV0GUAAADAev39/bumpibt27f/QscaIgr/z6uvvprJkyevNXsoAAAAwGcxRJStRm1tbUaOHLnW9pKSkkydOvUzjx0xYkSeeOKJjBgxIi1bttxYJQIAAABbIQEbW42jjz46Rx999Jc69uqrr15nW7du3RpseCgAAACw+TNEFAAAAACKIGADAAAAgCII2AAAAACgCL7BBluIZk1L0rjplvWTbd5sy6oXAAAAvgxvv7CFuGxw17Rv376hy/jCVtbVp0mpzrIAAABsvbz1AhuVcA0AAICtnTdfAAAAACiCgA0AAAAAiiBgAwAAAIAiCNgAAAAAoAgCNgAAAAAogoANAAAAAIogYAMAAACAIgjYAAAAAKAIAjYAAAAAKIKADQAAAACKIGADAAAAgCII2AAAAACgCAI2AAAAACiCgA0AAAAAiiBgAwAAAIAiCNgAAAAAoAgCNgAAAAAogoANAAAAAIogYAMAAACAIgjYAAAAAKAIAjYAAAAAKIKADQAAAACKIGADAAAAgCII2AAAAACgCAI2AAAAACiCgA3YbKysq2/oEgAAAOALK23oAoDPZ+Ttf07j5nMauoyNpnmz0vzm0sMaugwAAAD4wgRssIVYtnxVGpXUNXQZAAAAwD8xRBQAAAAAiiBgAwAAAIAiCNgAAAAAoAgCNgAAAAAogoANAAAAAIogYAMAAACAIgjY2Cz16tUrkyZNSpKMGTMmw4cPb+CKAAAAAD5daUMXwNbthRdeyLXXXpuZM2cmSXbeeeecddZZ6dGjx2ce98gjj3zua8ybNy+9evUqrC9dujRlZWUpKSlJklx22WU5+uijv0T16/fOO+9k9OjRmTJlSpYsWZJdd9015557brp165bkk/sfM2ZMXnnllSRJp06dctFFF2XnnXfeKPUAAAAAm56AjY3mo48+ypAhQ3LRRReld+/eqaury7Rp0wrB14bStm3bvPjii4X17t275xe/+EW6dOmyQa/zaT7++ON06NAhI0aMSKtWrfKf//mfOf300/Poo49mxx13zF//+tccd9xx2X///dO0adP84he/yOmnn56HHnpoo9cGAAAAbBqGiLLRzJ49O3V1denbt29KS0vTrFmzdOnSJZWVlUmSmpqaHHbYYamqqsqJJ56Y2bNnF47t2bNnJkyYsEHqWLhwYTp06JB33323sO3tt99Ohw4d8sEHH2TSpEk54IADctttt2W//fZLjx49cvfddxf2ra+vzx133JFDDz001dXVGTJkSBYuXJgk+da3vpWTTjoprVu3TuPGjXPEEUekoqIiM2bMSJIceOCBOfLII/P1r389ZWVlOemkkzJz5sz87W9/W6vORYsWZcaMGWsts2bN2iDPAQAAANg49GBjo9l1111TVlaWc889N0cddVT22WeftGzZMkny/PPPZ/To0bn99tuz11575ZZbbsmPf/zjPPzww2nSpMkGraN169aprq7OQw89lBNPPDFJUltbmwMPPDDbbrttkmTx4sVZuHBhJkyYkFdffTUnn3xy9thjj1RWVuauu+7K448/nl//+tdp1apVbrjhhpx77rn5j//4j7WuNX/+/Lz99tv5H//jf3xqLVOmTEmbNm3y9a9/fa22sWPH5sYbb9xwNw4AAABsEnqwsdGUl5fn97//fcrKynL55Zene/fuGTRoUN58883U1tamb9++6dixY8rKynL66afngw8+yLRp0zZKLd/97nczfvz4wnptbW369Omzxj7nnHNOysrKsvfee+eYY45JbW1tkuSee+7J8OHD881vfjNlZWU588wz8+KLLxZ6sf3d8uXLM3z48AwYMCC77LLLWjW8+eabufLKK3PRRRd9ao39+/dPTU3NWss111xT5N0DAAAAG5MebGxU7dq1y+jRo5N8Mixz5MiRueCCC1JeXp7999+/sF9paWnatGmTRYsWbZQ6DjnkkIwaNSozZ87M0qVL8+GHH+aAAw4otH/jG99IixYtCutt27bNlClTkiRz587NsGHD0rjx/8+jS0pKsmDBgrRu3TpJsmLFipxxxhlp06ZNfvKTn6x1/Xnz5uWkk07KaaedlsMOO+xTa6yoqEhFRcUGuV8AAABg0xGwscnstNNOOeGEE3LOOefkyCOPzLx58wptq1atyoIFCzZawFRWVpYjjzwy48aNy7Jly3LUUUetMRT1r3/9a5YsWVII2ebPn1+oZccdd8zo0aML3477ZytWrMiwYcNSVlaWa6+9dq1JHObPn59Bgwbl+OOPz8CBAzfK/QEAAAANxxBRNppZs2bljjvuyLx587J69eq8++67ue+++9KpU6f07t0748aNy8svv5wVK1bk5ptvzjbbbJN99tlno9XTr1+/PPTQQ3n00UfTt2/ftdqvv/76rFixItOnT8/48eNz1FFHJUkGDBiQ66+/Pm+99VaS5MMPP8xjjz2W5JNw7cwzz8zq1atz/fXXp7R0zcx6wYIFGThwYPr165cf/ehHG+3eAAAAgIajBxsbTXl5eaZPn5677rorf/vb39KiRYvst99+GTVqVCoqKnLeeeflvPPOy7vvvluY6GBDT3Dwj/bee+9ss802adSoUfbaa6812nbYYYdUVFTkgAMOSFlZWYYNG5bq6uokyYknnpjGjRtn8ODBWbhwYbbddtt07949RxxxRP73//7fefrppwszpP7dFVdckSOPPDL33ntv3nrrrdx+++25/fbbC+2PP/54YXgpAAAAsGVrtHr16tUNXQRsKkOGDEmXLl1y8sknF7ZNmjQpF154YSZMmNCAla3bjBkz0q9fv+zW85w0at6mocvZaL7WtDR/uLJXQ5cBAADAV9Tf379ramrSvn37L3SsIaJ8Zbz66quZPHnyWrOHAgAAABTDEFG2GLW1tRk5cuRa20tKSjJ16tTPPHbEiBF54oknMmLEiLRs2XJjlQgAAAB8BQnY2GIcffTROfroo7/UsVdfffU627p167bZDg8FAAAANn+GiAIAAABAEQRsAAAAAFAEARsAAAAAFME32GAL0axpSRo33Xp/ss2bbb33BgAAwNbNGy1sIS4b3DXt27dv6DI2qpV19WlSqmMtAAAAWxZvssBmQ7gGAADAlsjbLAAAAAAUQcAGAAAAAEUQsAEAAABAEQRsAAAAAFAEARsAAAAAFEHABgAAAABFELABAAAAQBEEbAAAAABQBAEbAAAAABRBwAYAAAAARRCwAQAAAEARBGwAAAAAUAQBGwAAAAAUQcAGAAAAAEUQsAEAAABAEQRsAAAAAFAEARsAAAAAFEHABgAAAABFELABAAAAQBEEbAAAAABQBAEbAAAAABRBwAYAAAAARRCwAQAAAEARBGwAAAAAUAQBGwAAAAAUQcAGsBlbWVff0CUAAACwHqUNXQDw+Yy8/c9p3HxOQ5fBJtS8WWl+c+lhDV0GAAAA6yFggy3EsuWr0qikrqHLAAAAAP6JIaIAAAAAUAQBGwAAAAAUQcAGAAAAAEUQsAEAAABAEQRsAAAAAFAEARsAAAAAFEHABgAAAABFELB9RZ1yyim57777kiQ1NTX53ve+t859hw8fnjFjxmyq0gAAAAC2KAK2rcStt96agQMHrrFtwIABOeKII9bYNnz48Fx55ZW54447ctxxx23KEjcb//Vf/5U99tgjd9999ya75uuvv54OHTpk+PDhm+yaAAAAwKYhYNtKVFdXZ9q0aVmxYkWSZPny5Xn99dezdOnSLF68uLDflClTUl1dvUlqWr16derq6jbJtb6ImpqabLPNNnnggQc+c7+VK1dusGuOGjUqHTt23GDnAwAAADYfAratRIcOHVJSUpKXXnopSfLiiy+mffv2qaqqypQpU5Iks2fPzuLFi1NZWZmBAwfmnnvu+dRzPffcc+nVq1c6deqUCy64YK2g6dlnn02/fv1SWVmZvn37ZurUqYW2gQMH5rrrrssJJ5yQjh07Zvr06bnwwgszatSonHHGGenUqVN69+6dv/zlL4Vj3nnnnZx99tnp1q1bevTokTFjxqS+vj5J8sYbb2TgwIHp3LlzunTpkqFDhyb5JLwbPXp0unbtmn333TdHHXVUZsyYsd7ntHjx4kyYMCGXX355Xnnllbz22muFtrfffjt77LFH7r///vTs2TN9+vRJksyZMyennHJKunTpkkMOOWSNnm8vv/xyvv/976eysjLdu3fPqFGjCiHn39XU1KSiomK9weaiRYsyY8aMtZZZs2at974AAACAhiNg20qUlpamU6dOmTx5cpJk6tSpqaysXCNgmzJlSnbfffdsu+226zzPBx98kKFDh2bw4MGZMmVKunbtmqeeeqrQ/uqrr+a8887LRRddlMmTJ+ess87K6aefnvfee6+wz4MPPpiLL744L7zwQvbaa68kySOPPJKTTz45U6dOzX777Zef//znSZL6+vqcdtpp+da3vpWnn346f/jDH/Lkk0/m/vvvT5L88pe/TLt27TJ58uQ8++yzOfnkk5MkEydOzOOPP57a2tpMnTo1Y8aMyfbbb7/e51RbW5uddtophx9+eCorK1NTU7PWPhMnTkxtbW0eeOCBLF26NCeddFJ69uyZiRMn5rbbbsvtt9+eP/3pT0mSkpKSXHDBBXnuuecyduzYPP/88/nd735XONf777+fW265JSNGjFhvbWPHjk2/fv3WWs4///z1HgsAAAA0HAHbVqS6uroQpk2ePDlVVVWprKxcY9v6elE988wz2WWXXdKnT5+Ulpamb9++2X333Qvt9957b4499thUVlamcePGOfDAA7PnnntmwoQJhX369OmTb3/72ykpKUlZWVmS5OCDD07nzp1TUlKSPn365JVXXkmSTJ8+PQsWLMjZZ5+dpk2bpnXr1jnxxBPz8MMPJ/kkOHznnXcyf/78lJWVpbKysrB9+fLl+e///u+sWrUqu+66a9q0abPeZ/Tggw+md+/eSZJjjjkmtbW1a/XQGzZsWMrLy9OsWbM8/fTT2WGHHXL88cenSZMm+dd//dccd9xxhfr22muvdO7cOaWlpdlpp53Sv3//wvNOkquvvjqDBg1Kq1at1ltb//79U1NTs9ZyzTXXrPdYAAAAoOGUNnQBbDhVVVX51a9+lY8//jivvvpq9tlnn5SVleX999/P+++/nylTpuTiiy/+zHMsWrQoO+644xrb2rZtW/h77ty5mTx5csaOHVvYVldXl+7duxfW//n4JNlhhx0Kfzdr1iwff/xxkk+GZb733nupqqoqtNfX1xfOcf755+eGG27IgAED0qJFiwwaNCg/+MEP0rVr1wwdOjTXXHNN3nrrrfTs2TM/+clPPrN33ksvvZSZM2fmpptuSpIcfvjh+dnPfpY//vGPOeSQQ9Z5v3/5y18KwV6SrFq1qrA+e/bsXHXVVZk+fXqWLl2aVatWZc8990zySaA5c+bMXHHFFeus6R9VVFSkoqLic+0LAAAAbD4EbFuRvffeO0lyzz33ZLfddiv0HuvcuXNqamqycOHCNYKiT1NRUZH58+evsW3+/Pn59re/neST8OyUU07JsGHD1nmOxo0/f8fItm3bpk2bNmsMQ/1HrVq1ys9+9rMkn3xX7oc//GGqqqqy++67Z9CgQRk0aFDee++9DB8+PLfeeutnDsWsqalJo0aNMmDAgMK2VatW5YEHHlgjYPvH+tu2bZtOnTqtMezzH40aNSq77757rrvuupSXl+e3v/1tHnnkkSTJ888/n9dffz3dunVLkixbtiyrVq3KIYcckieeeOJzPiEAAABgc2eI6FakSZMm6dixY+688841grSqqqrceeed2W233dKyZcvPPEePHj0yZ86cPPTQQ6mrq8u4ceMyc+bMQnv//v0zduzYTJ06NfX19Vm2bFmee+65LFiw4EvV3KFDh2y33Xa56aab8vHHH6e+vj5z5swpfEvuscceK5y7vLw8jRo1KkzmMG3atKxcuTLNmjVLWVlZSkpK1nmd5cuX59FHH81Pf/rTjBs3rrDccMMNmTBhwhozrf6jAw88MHPnzs19992XFStWpK6uLq+99lphMoklS5akvLw8LVq0yOzZs3PvvfcWjj3ppJPy+OOPZ/z48Rk/fny+//3vZ//9919jkgQAAABgyydg28pUV1dn8eLFawy5rKqqyuLFi9f7/bUk2W677XLjjTfmlltuSVVVVSZNmpSDDjqo0N6+fftcffXVueaaa9KlS5ccdNBB+fWvf12Y9fOLKikpya9+9au88cYbOfTQQ1NVVZWzzz4777zzTpJPvtF23HHHpVOnThkyZEguuOCCtGvXLkuWLMlPf/rTVFdX56CDDkp5eXmGDBmyzus88cQTadSoUY499ti0atWqsBx88MHZZZddUltb+6nHtWjRInfeeWeeeuqp9OjRI127ds2ll16aJUuWJElGjBiRxx57LJ07d87FF1+cww8/vHBseXl52rRpU1j+/l231q1bf6lnBQAAAGyeGq1evXp1QxcBrNuMGTPSr1+/7NbznDRqvv6JHNh6fK1paf5wZa+GLgMAAOAr4e/v3zU1NWnfvv0XOlYPNgAAAAAogkkO2Kr06tUr8+bNW2v70KFDM3jw4AaoCAAAANjaCdjYqvx9Bk8AAACATcUQUQAAAAAogoANAAAAAIpgiChsIZo1LUnjpn6yXyXNm/n3BgAA2BJ4e4MtxGWDu37haYLZ8q2sq0+TUp2NAQAANmfe2gA2Y8I1AACAzZ83NwAAAAAogoANAAAAAIogYAMAAACAIgjYAAAAAKAIAjYAAAAAKIKADQAAAACKIGADAAAAgCII2AAAAACgCAI2AAAAACiCgA0AAAAAiiBgAwAAAIAiCNgAAAAAoAgCNgAAAAAogoANAAAAAIogYAMAAACAIgjYAAAAAKAIAjYAAAAAKIKADQAAAACKIGADAAAAgCII2AAAAACgCAI2AAAAACiCgA0AAAAAiiBgAwAAAIAiCNgAAAAAoAgCNgAAAAAogoANYDO2sq6+oUsAAABgPUobugDg8xl5+5/TuPmchi6DTah5s9L85tLDGroMAAAA1kPABluIZctXpVFJXUOXAQAAAPwTQ0QBAAAAoAgCNgAAAAAogoANAAAAAIogYAMAAACAIgjYAAAAAKAIAjYAAAAAKIKADQAAAACKIGCDT/H888+ne/fuDV0GAAAAsAUobegCoCG98MILufbaazNz5swkyc4775yzzjorzZo1a+DKAAAAgC2FgI2vrI8++ihDhgzJRRddlN69e6euri7Tpk1LSUlJVq1a1dDlAQAAAFsIARtfWbNnz05dXV369u2bJCktLU2XLl2SfDJE9J/3veyyyzJjxoy0bNkyQ4YMSb9+/ZIkY8aMyWuvvZZmzZrlySefTNu2bTNy5MhUV1cn+STI+7d/+7c888wzWbVqVQ477LCMGDEiTZs2XeMaixYtyjvvvLNWnbNmzdrg9w4AAABsOAI2vrJ23XXXlJWV5dxzz81RRx2VffbZJy1btlxrv5UrV+bHP/5xjjzyyNx222155ZVXcsopp2SnnXYqhGhPPfVUrrzyylx11VV56KGHMnTo0Dz55JPZZptt8pOf/CQtWrTIo48+mvr6+gwfPjw333xzhg8fvsZ1xo4dmxtvvHGT3DsAAACw4ZjkgK+s8vLy/P73v09ZWVkuv/zydO/ePYMGDcqbb765xn7Tpk3LBx98kDPOOCNlZWXp2LFj+vbtm/Hjxxf22XPPPdOnT5+Ulpamb9++2WmnnfLMM8/k3XffzVNPPZVLLrkk5eXl+cY3vpHTTjstDz/88Fr19O/fPzU1NWst11xzzUZ/FgAAAMCXpwcbX2nt2rXL6NGjkyRvv/12Ro4cmQsuuGCN3mWLFi3KjjvumJKSksK2b37zm5k4cWJhfccdd1zjvG3bts3ChQszd+7crFq1KgceeGChbfXq1amvr1+rloqKilRUVGyoWwMAAAA2EQEb/D877bRTTjjhhJxzzjlrbK+oqMiCBQuyatWqQsg2d+7ctG7durDP/Pnz1zhm/vz5Oeyww7LjjjumtLQ0kyZNSllZ2ca/CQAAAGCTM0SUr6xZs2bljjvuyLx587J69eq8++67ue+++9KpU6c19ttnn33yjW98I7feemtWrFiRl156KePGjUvv3r0L+7z66qt56KGHUldXl3HjxuXNN99Mjx490qpVq/To0SNXXHFFPvzww6xevTrz58/PhAkTNvXtAgAAABuJgI2vrPLy8kyfPj3f//7307lz5xxzzDFp3rx5rrrqqjX2a9KkSW655ZY899xz6datW84///xccMEF2W+//Qr79OzZM3/84x9TVVWV2267LTfeeGO23XbbJMnVV1+dJk2apE+fPtl3333zox/9KHPmzNmUtwoAAABsRIaI8pXVunXr/Pu///untlVUVORPf/pTYb1du3a566671nmuJk2a5Nprr/3UtvLy8lxyySW55JJLiisYAAAA2CzpwQYAAAAARRCwAQAAAEARDBGFIg0bNqyhSwAAAAAakB5sAAAAAFAEARsAAAAAFEHABgAAAABF8A022EI0a1qSxk39ZL9Kmjfz7w0AALAl8PYGW4jLBndN+/btG7oMNrGVdfVpUqqzMQAAwObMWxvAZky4BgAAsPnz5gYAAAAARRCwAQAAAEARBGwAAAAAUAQBGwAAAAAUQcAGAAAAAEUQsAEAAABAEQRsAAAAAFAEARsAAAAAFEHABgAAAABFELABAAAAQBEEbAAAAABQBAEbAAAAABRBwAYAAAAARRCwAQAAAEARBGwAAAAAUAQBGwAAAAAUQcAGAAAAAEUQsAEAAABAEQRsAAAAAFAEARsAAAAAFEHABgAAAABFELABAAAAQBEEbAAAAABQBAEbAAAAABRBwAYAAAAARRCwAbBOK+vqG7oEAACAzV5pQxcAfD4jb/9zGjefC7/dlQAAIABJREFU09Bl8BXSvFlpfnPpYQ1dBgAAwGZPwAZbiGXLV6VRSV1DlwEAAAD8E0NEAQAAAKAIAjYAAAAAKIKADQAAAACKIGADAAAAgCII2AAAAACgCAI2AAAAACiCgA0AAAAAiiBg40u79NJLc8MNNyRJnn/++XTv3r3Q1rNnz0yYMOEzj3/77bezxx57ZPny5RuknmuvvTYXXnjhBjkXAAAAwOclYGOdBg4cmHvuuWeNbf8Yil1++eU588wzG6i6jWPlypW55pprcsABB2TffffN+eefnyVLljR0WQAAAMBmrLShC4DNyR133JEpU6bkwQcfTNOmTTN8+PD8/Oc/z+jRozf6tRctWpR33nlnre2zZs3a6NcGAAAAvjw92PjSLrzwwlx77bXr3e+ll17Kd7/73XTu3DndunXLZZddtkb7Y489lp49e6aqqipXXnllYftbb72VH/7wh+nSpUu6dOmSc845Jx9++GGh/dVXX813v/vddOrUKaeeemr++te/rnXdH/zgB6mqqsoRRxyR//W//td6a33iiScycODAbL/99ikvL8/gwYPz8MMPZ9myZUmScePGpVevXunUqVMOPvjg3H333YVj/z5M9je/+U2+853vpLq6Or///e8zc+bM9OvXL507d86ZZ55ZONc/Gzt2bPr167fWcv7556+3bgAAAKDh6MHGRnfFFVfk0EMPzamnnpply5bltddeW6P9+eefz8MPP5x33303ffv2zUEHHZSuXbsmSQYPHpzq6up89NFHGTZsWH75y1/m0ksvzcqVKzN06NB873vfy49+9KM8//zzOf3003PEEUck+aQ32ODBg3PFFVfkoIMOyvTp0zN48OC0a9cu7dq1W2etq1evzurVq9dYX7FiRebMmZM999wz2223XW6++ebsvPPOmTJlSgYPHpwOHTpk7733TpK8//77ef/99/P0009nypQpOfXUU7P//vvnlltuSfPmzTNgwIA88MAD+cEPfrDWtfv375+ePXuutX3WrFlCNgAAANiMCdj4TFdddVWuu+66wnp9ff0XPkdpaWnmzp2bRYsWpXXr1unYseMa7aeffnqaN2+e5s2bp7KyMq+88kq6du2af/mXf8m//Mu/JElatmyZk046Kb/85S+TJP/n//yfLF26NKeeemoaN26c73znO/nOd75TOOf48ePTtWvXHHLIIUmSffbZJ4ccckgee+yxnHHGGeus9YADDshdd92V/fbbL02bNs1tt92WJFm6dGmSpEePHoV9q6ur071790ydOrUQsDVu3DhnnHFGmjRpkm7duuXrX/96Dj300LRu3bpw/ldeeeVTr11RUZGKiorP/2ABAACAzYKAjc904YUXZsCAAYX1t99+OwcffPAXOscVV1yRMWPG5Oijj07r1q1z2mmnFXqaJUmrVq0Kf3/ta1/Lxx9/nCR599138/Of/zxTp07NkiVLsnr16rRo0SJJCmFd48b/f5Rz27Zt87e//S1JMnfu3Dz55JOprKwstK9atSpHH330Z9b64x//OB999FGOO+64NGrUKCeffHImTpyYNm3aJEn++Mc/5qabbsqcOXNSX1+fZcuWrdEjbptttkmTJk3WuJ/tt99+jfX58+d//ocHAAAAbPYEbGx0u+yyS6677rrU19fnqaeeyllnnZVu3bqt97i/H1NbW5vtttsuTzzxREaOHJnkk95eCxcuTH19fSFkmz9/fsrLy5N8Erb16tUrV1111ReqtVmzZrnkkktyySWXJEkmTJiQNm3apHXr1lmxYkXOPPPMXHnllTn00EPTpEmTnH766WsMKQUAAAC+ekxywEY3fvz4vPfee2ncuHGhB1pJScl6j1uyZEmaN2+er3/961m4cGHuvPPOQlvHjh3zta99LXfccUdWrlyZSZMm5dlnny20H3300ZkwYUKeeuqp1NXVZcWKFZk2bdp6Z+RcuHBhFixYkNWrV+e1117LVVddlWHDhqVx48ZZsWJFVqx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      "text/plain": [
       "<Figure size 1280x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(16, 18), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "feat_importances = pd.Series(trainedforest.feature_importances_, index= X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Reduced = df[['Elevation','Horizontal_Distance_To_Roadways', 'Horizontal_Distance_To_Fire_Points','Horizontal_Distance_To_Hydrology']]\n",
    "X_Reduced = StandardScaler().fit_transform(X_Reduced)\n",
    "X_Train2, X_Test2, Y_Train2, Y_Test2 = train_test_split(X_Reduced, Y, test_size = 0.30, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "976.5061799249997\n",
      "[[59039  4174     4     0    22     5   155]\n",
      " [ 3311 80958   393     2   178   135    16]\n",
      " [    2   499  9699   104    12   506     0]\n",
      " [    0     0   207   563     0    59     0]\n",
      " [   72   713    24     0  2046    13     2]\n",
      " [    5   424   740    37    11  4032     0]\n",
      " [  477    49     0     0     1     0  5615]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.94      0.93      0.93     63399\n",
      "           2       0.93      0.95      0.94     84993\n",
      "           3       0.88      0.90      0.89     10822\n",
      "           4       0.80      0.68      0.73       829\n",
      "           5       0.90      0.71      0.80      2870\n",
      "           6       0.85      0.77      0.81      5249\n",
      "           7       0.97      0.91      0.94      6142\n",
      "\n",
      "    accuracy                           0.93    174304\n",
      "   macro avg       0.90      0.84      0.86    174304\n",
      "weighted avg       0.93      0.93      0.93    174304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train2,Y_Train2)\n",
    "print(time.process_time() - start)\n",
    "predictionforest = trainedforest.predict(X_Test2)\n",
    "print(confusion_matrix(Y_Test2,predictionforest))\n",
    "print(classification_report(Y_Test2,predictionforest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SelectFromModel: Meta-transformer for selecting features based on importance weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SelectFromModel is another Scikit-learn method which can be used for Feature Selection. This method can be used with all the different types of Scikit-learn models (after fitting) which have a coef_ or featureimportances attribute. Compared to RFE, SelectFromModel is a less robust solution. In fact, SelectFromModel just removes less important features based on a calculated threshold (no optimization iteration process involved).\n",
    "\n",
    "In order to test SelectFromModel efficacy, I decided to use an ExtraTreesClassifier in this example. ExtraTreesClassifier (Extremely Randomized Trees) is tree based ensamble classifier which can yield less variance compared to Random Forest methods (reducing therefore the risk of overfitting). The main difference between Random Forest and Extremely Randomized Trees is that in Extremely Randomized Trees nodes are sampled without replacement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.76214510400041\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(406708, 12)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "model = ExtraTreesClassifier()\n",
    "start = time.process_time()\n",
    "model = model.fit(X_Train,Y_Train)\n",
    "model = SelectFromModel(model, prefit=True)\n",
    "print(time.process_time() - start)\n",
    "Selected_X = model.transform(X_Train)\n",
    "Selected_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1165.7185971490003\n",
      "[[59516  3766     1     0    11     7    98]\n",
      " [ 2346 82278   167     6    98    80    18]\n",
      " [    0   277 10269    57     4   215     0]\n",
      " [    0     1   108   693     0    27     0]\n",
      " [   35   817    29     0  1982     7     0]\n",
      " [    4   279   398    17     2  4549     0]\n",
      " [  397    33     0     0     0     0  5712]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.96      0.94      0.95     63399\n",
      "           2       0.94      0.97      0.95     84993\n",
      "           3       0.94      0.95      0.94     10822\n",
      "           4       0.90      0.84      0.87       829\n",
      "           5       0.95      0.69      0.80      2870\n",
      "           6       0.93      0.87      0.90      5249\n",
      "           7       0.98      0.93      0.95      6142\n",
      "\n",
      "    accuracy                           0.95    174304\n",
      "   macro avg       0.94      0.88      0.91    174304\n",
      "weighted avg       0.95      0.95      0.95    174304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(Selected_X, Y_Train)\n",
    "print(time.process_time() - start)\n",
    "Selected_X_Test = model.transform(X_Test)\n",
    "predictionforest = trainedforest.predict(Selected_X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature 0 (0.312920)\n",
      "2. feature 5 (0.143248)\n",
      "3. feature 9 (0.133684)\n",
      "4. feature 3 (0.071373)\n",
      "5. feature 4 (0.067562)\n",
      "6. feature 1 (0.050592)\n",
      "7. feature 7 (0.046124)\n",
      "8. feature 6 (0.042763)\n",
      "9. feature 8 (0.042066)\n",
      "10. feature 10 (0.038593)\n",
      "11. feature 2 (0.034919)\n",
      "12. feature 11 (0.016157)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html\n",
    "importances = trainedforest.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in trainedforest.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(Selected_X.shape[1]):\n",
    "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(Selected_X.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(Selected_X.shape[1]), indices)\n",
    "plt.xlim([-1, Selected_X.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Matrix Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Seaborn, we can now plot the Pearson correlation heatmap of our dataset. Inspecting this plot, we can then be able to see the correlation of our independent variables (X) with our label (Y). Finally, we can then select just the features which are most correlated with Y and train/test an SVM model to test the results of this approach.\n",
    "\n",
    "Using Pearson correlation our returned coefficient values will vary between -1 and 1:\n",
    "\n",
    "- If the correlation between two features is 0 this means that changing any of these two features will not affect the other.\n",
    "- If the correlation between two features is greater than 0 this means that increating the values in one feature will make increase also the values in the other feature (the closer the correlation coefficient is to 1 and the stronger is going to be this bond between the two different features).\n",
    "- If the correlation between two features is less than 0 this means that increating the values in one feature will make decrease the values in the other feature (the closer the correlation coefficient is to -1 and the stronger is going to be this relationship between the two different features).\n",
    "\n",
    "Another possible aspect to control in this analysis would be to check if the selected variables are highly correlated each other. If they are, we would then need to keep just one of the correlated ones and drop the others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Elevation</th>\n",
       "      <th>Aspect</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology</th>\n",
       "      <th>Vertical_Distance_To_Hydrology</th>\n",
       "      <th>Horizontal_Distance_To_Roadways</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Hillshade_3pm</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points</th>\n",
       "      <th>Wilderness_Area1</th>\n",
       "      <th>Wilderness_Area2</th>\n",
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       "      <th>Soil_Type3</th>\n",
       "      <th>Soil_Type4</th>\n",
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       "      <th>Soil_Type6</th>\n",
       "      <th>Soil_Type7</th>\n",
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       "      <th>Soil_Type9</th>\n",
       "      <th>Soil_Type10</th>\n",
       "      <th>Soil_Type11</th>\n",
       "      <th>Soil_Type12</th>\n",
       "      <th>Soil_Type13</th>\n",
       "      <th>Soil_Type14</th>\n",
       "      <th>Soil_Type15</th>\n",
       "      <th>Soil_Type16</th>\n",
       "      <th>Soil_Type17</th>\n",
       "      <th>Soil_Type18</th>\n",
       "      <th>Soil_Type19</th>\n",
       "      <th>Soil_Type20</th>\n",
       "      <th>Soil_Type21</th>\n",
       "      <th>Soil_Type22</th>\n",
       "      <th>Soil_Type23</th>\n",
       "      <th>Soil_Type24</th>\n",
       "      <th>Soil_Type25</th>\n",
       "      <th>Soil_Type26</th>\n",
       "      <th>Soil_Type27</th>\n",
       "      <th>Soil_Type28</th>\n",
       "      <th>Soil_Type29</th>\n",
       "      <th>Soil_Type30</th>\n",
       "      <th>Soil_Type31</th>\n",
       "      <th>Soil_Type32</th>\n",
       "      <th>Soil_Type33</th>\n",
       "      <th>Soil_Type34</th>\n",
       "      <th>Soil_Type35</th>\n",
       "      <th>Soil_Type36</th>\n",
       "      <th>Soil_Type37</th>\n",
       "      <th>Soil_Type38</th>\n",
       "      <th>Soil_Type39</th>\n",
       "      <th>Soil_Type40</th>\n",
       "      <th>Y</th>\n",
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       "      <td>0</td>\n",
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       "      <td>221</td>\n",
       "      <td>232</td>\n",
       "      <td>148</td>\n",
       "      <td>6279</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2590</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "      <td>-6</td>\n",
       "      <td>390</td>\n",
       "      <td>220</td>\n",
       "      <td>235</td>\n",
       "      <td>151</td>\n",
       "      <td>6225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2804</td>\n",
       "      <td>139</td>\n",
       "      <td>9</td>\n",
       "      <td>268</td>\n",
       "      <td>65</td>\n",
       "      <td>3180</td>\n",
       "      <td>234</td>\n",
       "      <td>238</td>\n",
       "      <td>135</td>\n",
       "      <td>6121</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2785</td>\n",
       "      <td>155</td>\n",
       "      <td>18</td>\n",
       "      <td>242</td>\n",
       "      <td>118</td>\n",
       "      <td>3090</td>\n",
       "      <td>238</td>\n",
       "      <td>238</td>\n",
       "      <td>122</td>\n",
       "      <td>6211</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2595</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>-1</td>\n",
       "      <td>391</td>\n",
       "      <td>220</td>\n",
       "      <td>234</td>\n",
       "      <td>150</td>\n",
       "      <td>6172</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Elevation  Aspect  Slope  Horizontal_Distance_To_Hydrology  \\\n",
       "0       2596      51      3                               258   \n",
       "1       2590      56      2                               212   \n",
       "2       2804     139      9                               268   \n",
       "3       2785     155     18                               242   \n",
       "4       2595      45      2                               153   \n",
       "\n",
       "   Vertical_Distance_To_Hydrology  Horizontal_Distance_To_Roadways  \\\n",
       "0                               0                              510   \n",
       "1                              -6                              390   \n",
       "2                              65                             3180   \n",
       "3                             118                             3090   \n",
       "4                              -1                              391   \n",
       "\n",
       "   Hillshade_9am  Hillshade_Noon  Hillshade_3pm  \\\n",
       "0            221             232            148   \n",
       "1            220             235            151   \n",
       "2            234             238            135   \n",
       "3            238             238            122   \n",
       "4            220             234            150   \n",
       "\n",
       "   Horizontal_Distance_To_Fire_Points  Wilderness_Area1  Wilderness_Area2  \\\n",
       "0                                6279                 1                 0   \n",
       "1                                6225                 1                 0   \n",
       "2                                6121                 1                 0   \n",
       "3                                6211                 1                 0   \n",
       "4                                6172                 1                 0   \n",
       "\n",
       "   Wilderness_Area3  Wilderness_Area4  Soil_Type1  Soil_Type2  Soil_Type3  \\\n",
       "0                 0                 0           0           0           0   \n",
       "1                 0                 0           0           0           0   \n",
       "2                 0                 0           0           0           0   \n",
       "3                 0                 0           0           0           0   \n",
       "4                 0                 0           0           0           0   \n",
       "\n",
       "   Soil_Type4  Soil_Type5  Soil_Type6  Soil_Type7  Soil_Type8  Soil_Type9  \\\n",
       "0           0           0           0           0           0           0   \n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "3           0           0           0           0           0           0   \n",
       "4           0           0           0           0           0           0   \n",
       "\n",
       "   Soil_Type10  Soil_Type11  Soil_Type12  Soil_Type13  Soil_Type14  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            1            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   Soil_Type15  Soil_Type16  Soil_Type17  Soil_Type18  Soil_Type19  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   Soil_Type20  Soil_Type21  Soil_Type22  Soil_Type23  Soil_Type24  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   Soil_Type25  Soil_Type26  Soil_Type27  Soil_Type28  Soil_Type29  \\\n",
       "0            0            0            0            0            1   \n",
       "1            0            0            0            0            1   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            1   \n",
       "\n",
       "   Soil_Type30  Soil_Type31  Soil_Type32  Soil_Type33  Soil_Type34  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            1            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   Soil_Type35  Soil_Type36  Soil_Type37  Soil_Type38  Soil_Type39  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   Soil_Type40  Y  \n",
       "0            0  5  \n",
       "1            0  5  \n",
       "2            0  2  \n",
       "3            0  2  \n",
       "4            0  5  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Numeric_df = pd.DataFrame(X)\n",
    "Numeric_df['Y'] = Y\n",
    "Numeric_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Elevation           0.269554\n",
       "Wilderness_Area4    0.323200\n",
       "Y                   1.000000\n",
       "Name: Y, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "corr= Numeric_df.corr()\n",
    "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)\n",
    "\n",
    "# Selecting only correlated features\n",
    "corr_y = abs(corr[\"Y\"])\n",
    "highest_corr = corr_y[corr_y >0.25]\n",
    "highest_corr.sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Reduced2 = X[['Elevation','Wilderness_Area4']]\n",
    "X_Reduced2 = StandardScaler().fit_transform(X_Reduced2)\n",
    "X_Train3, X_Test3, Y_Train3, Y_Test3 = train_test_split(X_Reduced2, Y, test_size = 0.30, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "183.09225012000024\n",
      "[[41874 21525     0     0     0     0     0]\n",
      " [16968 67087   938     0     0     0     0]\n",
      " [    0  4317  6505     0     0     0     0]\n",
      " [    0     0   829     0     0     0     0]\n",
      " [    0  2870     0     0     0     0     0]\n",
      " [    0  2319  2930     0     0     0     0]\n",
      " [ 6113    29     0     0     0     0     0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.64      0.66      0.65     63399\n",
      "           2       0.68      0.79      0.73     84993\n",
      "           3       0.58      0.60      0.59     10822\n",
      "           4       0.00      0.00      0.00       829\n",
      "           5       0.00      0.00      0.00      2870\n",
      "           6       0.00      0.00      0.00      5249\n",
      "           7       0.00      0.00      0.00      6142\n",
      "\n",
      "    accuracy                           0.66    174304\n",
      "   macro avg       0.27      0.29      0.28    174304\n",
      "weighted avg       0.60      0.66      0.63    174304\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedsvm = svm.LinearSVC().fit(X_Train3, Y_Train3)\n",
    "print(time.process_time() - start)\n",
    "predictionsvm = trainedsvm.predict(X_Test3)\n",
    "print(confusion_matrix(Y_Test3,predictionsvm))\n",
    "print(classification_report(Y_Test3,predictionsvm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Univariate Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Univariate Feature Selection is a statistical method used to select the features which have the strongest relationship with our corrispondent labels. Using the SelectKBest method we can decide which metrics to use to evaluate our features and the number of K best features we want to keep. Different types of scoring functions are available depending on our needs:\n",
    "\n",
    "- Classification: chi2, f_classif, mutual_info_classif\n",
    "- Regression: f_regression, mutual_info_regression\n",
    "\n",
    "In this example, we will be using chi2. Chi-squared (Chi2) can take as input just non-negative values, therefore, first of all we scale our input data in a range between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "Scaled_X = min_max_scaler.fit_transform(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84.93948121299945\n",
      "[[    0 63399     0     0     0     0     0]\n",
      " [    0 84055   938     0     0     0     0]\n",
      " [    0  4317  6505     0     0     0     0]\n",
      " [    0     0   829     0     0     0     0]\n",
      " [    0  2870     0     0     0     0     0]\n",
      " [    0  2319  2930     0     0     0     0]\n",
      " [    0  6142     0     0     0     0     0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.00      0.00      0.00     63399\n",
      "           2       0.52      0.99      0.68     84993\n",
      "           3       0.58      0.60      0.59     10822\n",
      "           4       0.00      0.00      0.00       829\n",
      "           5       0.00      0.00      0.00      2870\n",
      "           6       0.00      0.00      0.00      5249\n",
      "           7       0.00      0.00      0.00      6142\n",
      "\n",
      "    accuracy                           0.52    174304\n",
      "   macro avg       0.16      0.23      0.18    174304\n",
      "weighted avg       0.29      0.52      0.37    174304\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "X_new = SelectKBest(chi2, k=2).fit_transform(Scaled_X, Y)\n",
    "X_Train3, X_Test3, Y_Train3, Y_Test3 = train_test_split(X_new, Y, test_size = 0.30, random_state = 101)\n",
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train3,Y_Train3)\n",
    "print(time.process_time() - start)\n",
    "predictionforest = trainedforest.predict(X_Test3)\n",
    "print(confusion_matrix(Y_Test3,predictionforest))\n",
    "print(classification_report(Y_Test3,predictionforest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lasso Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When applying regularization to a Machine Learning model, we add a penalty to the model parameters so that to avoid that our model tries to resemble too closely our input data. In this way, we can make our model less complex and we can avoid overfitting (making learn to our model not just the key data characheteristics but also it's intrinsic noise).\n",
    "\n",
    "One of the possible Regularization Methods is Lasso (L1) Regrssion. When using Lasso Regression, the coefficients of the inputs features gets shrinken if they are not positively contributing towards our Machine Learning model training. In this way, some of the features might get automatically discarded assigning them coefficients equal to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LassoCV Best Alpha Scored:  0.00045057699749182086\n",
      "LassoCV Model Accuracy:  0.32115743041655\n",
      "Variables Eliminated:  2\n",
      "Variables Kept:  52\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 139.66136089561041, tolerance: 79.27058967514648\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "regr = LassoCV(cv=5, random_state=101)\n",
    "regr.fit(X_Train,Y_Train)\n",
    "print(\"LassoCV Best Alpha Scored: \", regr.alpha_)\n",
    "print(\"LassoCV Model Accuracy: \", regr.score(X_Test, Y_Test))\n",
    "model_coef = pd.Series(regr.coef_, index = list(X.columns[:-1]))\n",
    "print(\"Variables Eliminated: \", str(sum(model_coef == 0)))\n",
    "print(\"Variables Kept: \", str(sum(model_coef != 0))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Most Important Features Identified using Lasso (!0)')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "top_coef = model_coef.sort_values()\n",
    "top_coef[top_coef != 0].plot(kind = \"barh\")\n",
    "plt.title(\"Most Important Features Identified using Lasso (!0)\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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